ict in agribusiness

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SPECIAL ISSUES OF THE MONITORING CENTRE:

ICT IN AGRIBUSINESS

SPECIAL ISSUES OF THE MONITORING CENTRE:

ICT IN AGRIBUSINESS

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ASSOCIATION FOR THE PROMOTION OF BRAZILIAN SOFTWARE EXCELLENCE – SOFTEX President Rubén Delgado Vice President of Operations Diônes Lima Vice President of Management and Finance Fabian Appel Petrait Head of Institutional Relations Carlos Alberto Leitão Technician in charge Virgínia Duarte Technical staff Antônio Carlos Diegues, José Eduardo Roselino, Paulo Roberto Villela e Virgínia Duarte Revision & Copyediting Softex Intelligence Dept. Promotion & dissemination Softex Communications MLP Assessoria e Consultoria Técnica de Imprensa Graphic design & typesetting WK Editorial SPECIAL RECOGNITION IT Policy Secretariat (SEPIN)/MCTIC ACKNOWLEDGEMENT Embrapa Informática Agropecuária: Ariovaldo Luchiari Jr.; Luiz Manoel Silva Cunha; Junia Alencar; Isaque Vacari; Glauber José Vaz; Débora Pignatari Drucker. Associação Brasileira das Empresas de Software (ABES): Anselmo Gentile; Jamile Sabatini Marques. TOTVS S/A.: Fábio Girardi; Fabio Cesar Turati. Sysvale Softgroup. The opinions expressed in this publication are the sole and entire responsibility of their authors and do not necessarily reflect the views of SOFTEX or its partners and interviewed individuals. The duplication or reproduction of this work in any medium is allowed only with Softex authorization. The ideas expressed in this publication may be reproduced provided the source is cited. All rights reserved to the Association for the Promotion of Brazilian Software Excellence – SOFTEX Copyright©2016 to SOFTEX Association for the Promotion of Brazilian Software Excellence – SOFTEX Rua Irmã Serafina, 863 – 6th floor Edifício Sada Jorge Downtown - Campinas, SP Brazil CEP: 13015-914 www.softex.br

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ABOUT SOFTEX The Association for the Promotion of Brazilian Software Excellence – SOFTEX – is a non-profit entity of the private sector which develops actions to promote the improvement of competitiveness of the Brazilian software and IT services industry. It manages the Program for the Promotion of Export of Brazilian Software – SOFTEX Program, deemed a priority program by the Ministry of Science, Technology and Innovation (MCTI). Since its inception in 1996, SOFTEX has expanded its area of operation, substantially contributing to the Brazilian social and economic development and to the competitive insertion of the country into the world economy. We have programs and operations in the following areas: innovation and entrepreneurship; investment; development of businesses abroad; quality; capacity-building with human resources; and business intelligence. The SOFTEX System is made up of SOFTEX and a team of Local Agents spread over 19 cities across 12 Brazilian states, which have over 2,000 member companies.

Fortaleza Campina Grande Recife

Salvador Brasília

Belo Horizonte Vitória São José do Rio Preto São Carlos Rio de Janeiro Londrina Campinas São Paulo Curitiba Blumenau Caxias do Sul

Joinville Florianópolis

Porto Alegre

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ABOUT NISB/SOFTEX MONITORING CENTRE The Brazilian Software Intelligence Center (NISB)/SOFTEX Monitoring Centre is SOFTEX’s study, research and intelligence unit. It is the responsibility of the unit to collect, arrange, analyze and disseminate data and information on the software activities and IT services carried out in Brazil. It is also within their scope to propose, implement and disseminate new concepts and methodologies for studies, to interact with universities and research institutes at the national and international levels and to encourage the emergence of research groups on topics of interest. The generation of Strategic and Competitive Intelligence for the software & IT services industry is an action made possible by maintaining and updating an Information System made up of reliable data from various official sources and market research. The NISB/SOFTEX Monitoring Centre’s activities also include studies on digital ecosystems and consulting on demand and the publication of the series Software and IT Services: The Brazilian Industry in Perspective and Special Issues of the Monitoring Centre.

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

SUMMARY INTRODUCTION

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CHAPTER 1 TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

9

José Eduardo Roselino & Antônio Carlos Diegues

CHAPTER 2 ICT IN AGRIBUSINESS: TECHNOLOGICAL TRENDS AND BUSINESS OPPORTUNITIES

41

Paulo Roberto de Castro Villela

CHAPTER 3 PROSPECTS AND PREDICTIONS FOR ICT IN BRAZILIAN AGRIBUSINESS

79

Virgínia Duarte

GLOSSARY

123

BIBLIOGRAPHICAL REFERENCES

127

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INTRODUCTION

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

INTRODUCTION Monsanto announced the purchase of Clima Corporation, a company with a solution combining agricultural insurance and data of interest to farmers. John Deere, DuPont and Dow Chemical joined to recommend, from data conveyed by agricultural machinery, the use of seeds, fertilizers, herbicides and other inputs to farmers. These are examples of strategic alliances recently established by large agribusiness companies. They show the growing interest of these companies for sources of information available which may allow the provision of dense business knowledge. They also point out to the increasing convergence between information and communication technologies (ICT) and the activities and processes of the agribusiness. AgroTIC, i.e., the adoption of ICT in agribusiness, refers precisely to this ongoing joint effort. It is the use of hardware, software, communication infrastructure and other agricultural equipment and machinery able to collect, convey, store, process and handle data and information and generate knowledge in agribusiness. Every year, ICT becomes more and more essential to the growth of agribusiness. It is in the foundation of the new wave of change in the countryside, which will provide greater gains in productivity, ensure more sustainable models of production and lead to a rational, efficient and effective management of the inputs available. These changes will be significant. In 2030, the agricultural production processes will be highly automated and grounded in knowledge.

ABOUT THIS STUDY This study seeks to evaluate the presence of ICT in the Brazilian agribusiness, the existing range of products and services for this market and key trends, opportunities and challenges in AgroTIC, especially for startups and small Brazilian companies from the software and IT services industry. Through a methodology of their own, based on the presence of ICT professionals in the production chains of agribusiness, the demand for AgroTIC in Brazil is estimated in Chapter 1, Technology and market dynamics for the software & ICT services chain focused on agribusiness. The supply of AgroTIC products and services is discussed in Chapter 2, ICT in agribusiness: technological trends and business opportunities. In this chapter are also listed the main technological trends, opportunities and key existing institutions and initiatives for the development of ICT in agribusiness.

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INTRODUCTION

Chapter 3, Prospects and predictions for ICT in Brazilian agribusiness, cites the technological trends and the new ICT business models and assesses their impact on the supply of AgroTIC. Pursuant to the effort already began in Chapter 2, Chapter 3 takes up the discussion on the state-of-the-art in AgroTIC, the key opportunities, the major challenges, the uncertainties concerning the future and predictions. Virgínia Duarte

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

CHAPTER

1

TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS INTRODUCTION Brazil is a major player in the global market of agricultural products and this position is deeply rooted in the historical process of formation of the national economy, formed from the beginning to meet the demand for primary products from the European economies. In the various cycles that followed from the colonial past to the crisis of the coffee economy in the first half of the twentieth century, the competitive advantages of the national export sectors rested on static determinants, essentially resulting from the natural endowments of factors. The extraordinary process of structural transformation that followed the crisis of the coffee economy has led, in the following decades, the establishment of a complex and integrated industrial matrix. The dynamic center of the Brazilian economy shifted from the countryside to the cities. Despite the success achieved with this process of rapid industrialization until the second half of the 1970s, a combination of unfavorable factors (domestic and foreign) prevented Brazil from succeeding in the catch up process related to the new leading sectors of the capitalist dynamics within the current techno-economic paradigm. In these terms, the long period of stagnation that followed the debt crisis in the brink of the 1980s created serious obstacles to the full development of activities related to the third industrial revolution and to the economics of information or knowledge. Among these representative sectors of the third industrial revolution, the development of the Brazilian industry of software and ICT services (information and communication technologies) appears as a particular success. Due to the competitive characteristics of this sector and to historical, structural and institutional aspects, this industry found virtuous conditions for its expansion and development, standing among the ten largest in the world (Roselino, 2006; Diegues, 2010). However, the Brazilian industrialization did not mean the loss of the absolute economic relevance of the land-related activities. To illustrate this, it is worth highlighting that Brazil keeps, for more than a century and up to the present times, the condition of largest coffee producer and exporter in the world.

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CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

Together with the static factors which determined the traditional national comparative advantages, Brazil saw the development of a set of dynamic factors able to boost agribusiness on a new basis from the second half of the twentieth century. The work of institutions such as Embrapa (Brazilian Agricultural Research Corporation), IAC (Agronomic Institute of Campinas) and CTC (Center for Sugarcane Technology), as well as academic institutions of excellence, resulted in the construction and dissemination of technological skills which promoted the productivity and efficiency of the Brazilian agribusiness. These institutions have had and still have an important role in the technological development of the Brazilian agribusiness, with initiatives aimed at developing new cultivars adapted to the Brazilian soil and climate conditions, genetic improvement of seeds, soil correction, proper use of pesticides and fertilizers, in addition to important extension initiatives aimed at disseminating the best practices to farmers. This context is precisely what explains the impetus to the modernization of the countryside and the increase in the Brazilian agricultural productivity, boosting the sector that previously showed a performance below that observed in developed and underdeveloped economies, since the last quarter of the twentieth century, as shown in Figure 1.1. FIGURE 1.1 – AVERAGE RATE OF GROWTH FOR AGRICULTURAL PRODUCTIVITY PER PERIOD (%) 5,00 4,00 3,00 2,00 1,00 0,00 -1,00

1961-69

1970-79

1980-89

1990-99

2000-07

-2,00 Developing Countries

Brazil

China

Source: Fostering Productivity and Competitiveness in Agriculture – OCDE (2011).

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Developed Countries

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

The adoption of ICT in agribusiness represents an opportunity to merge two sectors in which Brazil was able to build expressive skills. These combined aspects of the Brazilian productive structure, which combines the existence of a dynamic and competitive agribusiness with a significant industry of software and ICT services, allow the consideration of the high potential for virtuous combination of these activities. Products and services associated to the information and communication technologies are elements which promote efficiency and productivity in the most various sectors, playing the role of an increasingly important link in the various production chains. In this sense, the adoption of ICT is configured as a potential and promising vector for expansion of productivity in agribusiness in the world and in Brazil, with effects possibly comparable to those that the dissemination of processes of mechanization, irrigation and use of agrochemicals had in the past. The adoption of these technologies is even more relevant when considering the emergence of increasing pressure on the management of agribusiness related to environmental and phytosanitary requirements. Given this set of changes, land-related activities differ from the traditional condition of archaic industry and begin to progressively gather higher technological content and sophistication. The review proposed in this chapter intends to advance this diagnosis through an approach that combines a characterization of the value chain of ICT focused on agribusiness, introducing a typology of the technologies employed for this sector, followed by a brief overview with the large numbers of the Brazilian agribusiness and its insertion in the international market, and also an analysis of the adoption of ICT by part of this sector, in geographic/spatial terms, as well as by large segments. At the end of the chapter, we present our final thoughts, summarizing the positive aspects, the weaknesses, threats and opportunities related to the adoption of ICT in the Brazilian agribusiness.

1.1  VALUE CHAIN AND MARKET STRUCTURE OF ICT ACTIVITIES FOR AGRIBUSINESS The review proposed in this work, of characterizing the ICT chain dynamics applied to agribusiness, requires considering the significant heterogeneity in both parties involved: in the broad universe of ICT solutions (products and services), as well as in the agricultural complex, formed by business units and segments with distinct characteristics. Zambalde et al (2011) propose a typology aimed at grouping the different ICT demanded by the agribusiness, according to the nature of these technologies. This approach describes the existence of three broad categories, namely: a. Management & Administration Technologies Geared to organization and control of the production activity, supporting decision-making by owners or managers. This group brings together generic solutions such as spreadsheets, software for database and word processing, and also their own applications for management, such as financial, accounting, inventory, logistics, procurement, human resources and marketing systems. The group also includes vertical solutions specific to agribusiness, such as software for planning and controlling the production chain, calculation of production optimization, management of livestock, farm management, etc.

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b. Control, Monitoring and Robotics Technologies The group consists of computers, sensors, actuators, displays, equipment and software applied to agriindustrial management and precision processes in agriculture and animal husbandry, and by the network infrastructure. Examples are the systems of automatic irrigation, agricultural and animal traceability, electronic identification, geographic information (GIS), global positioning (GPS) and electronic sensors. c. Telecommunications and Internet Technologies This group combines the technologies that make the integration and the interaction possible, providing generation, combination and dissemination of information. They are related to transmission of data, voice and images (cell phones, digital TV and others). Among the applications are the monitoring of meteorological information, the movement of animals carried out by satellite, access to shared database, exchange of experiences and e-commerce. ICT penetration in agribusiness results from two distinct movements: an exogenous movement and an endogenous movement. The following is a specific effort to analyze the competitive and technological dynamics of the ICT market for agribusiness, from a new proposition of typology of the technologies demanded by this sector. The intention is to show that the various groups of technologies reserve distinct competitive dynamics and, as a result, provide different opportunities for new players to enter. The proposed typology considers that the penetration and dissemination of ICT usage by the agribusiness are driven by two different components/forces (Diegues & Roselino, 2012): the exogenous and the endogenous components (Box 1.1). Exogenous component: technological solutions for widespread use or developed for other sectors: basic infrastructure of ICT and horizontal applications. The first one of these forces, named exogenous component, relates to the movement of dissemination of use by agricultural enterprises of technological solutions for widespread use or initially developed for other sectors. This movement is related, therefore, to the adoption of two different groups of technologies by the agribusiness: technologies related to basic infrastructure of ICT, and technological solutions for horizontal use, such as personal/business productivity software and business management systems commonly used, such as ERP (Enterprise Resource Planning). Endogenous component: technological solutions to meet specific demands of the industry: vertical applications and embedded software. The second vector of dissemination of ICT with agribusiness is named the endogenous component, due to its origin in meeting the very own needs and demands of the sector. As it was with the exogenous component, it is also possible to identify, here, two main groups of technological solutions: vertical solutions, developed by ICT companies to meet specific demands of the industry or segments/agribusiness niches, or technologies embedded in microelectronic components of equipment and agricultural implements.

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

BOX 1.1 – SUMMARY OF COMPONENTS AND TECHNOLOGIES RELATED TO THE ADOPTION OF ICT IN AGRIBUSINESS COMPONENTS/FORCES

TYPE OF TECHNOLOGY

EXAMPLES

EXOGENOUS: movement of dissemination of technologies generated to meet demands from out of agribusiness

ICT general infrastructure

Data networks (stationary or moving); GPS

Horizontal/General purpose solutions

Spreadsheets; Databases; ERP

ENDOGENOUS: movement focused on the adoption of technologies that meet the specifics of agribusiness

ICT embedded in equipment / implements

Sensor Irrigation System; harvesters

Vertical solutions focused on market niches

Farm management system; animal tracking

Source: Softex Monitoring Centre.

There is a great heterogeneity in ICT employed in agribusiness, associated to different market structures and competitive dynamics. In exogenous technologies, it is the oligopolized market structures which are predominant. It is possible, based on this typology, to characterize, in general terms, the competitive/marketing aspects of each of the technology groups that make up this value chain, as well as the predominant types of players that command each of these market segments of ICT focused on agribusiness. In the case of technologies promoted by the exogenous component of dissemination of ICT, the walls to prevent new players from entering are higher, and the supply of these technologies tends to be focused on a few large companies (oligopolized market structures). The group of technologies associated with the basic ICT infrastructure is characterized by the absence of interactions with agribusiness claimants with regard to the development of solutions, since they are not usually designed to meet specifics of the industry. The supply structure related to the adoption of technologies associated with the basic infrastructure is usually concentrated and dominated by large, often global companies. This case includes communication service providers (voice and data), IT and communication equipment manufacturers (hardware) and software developers and providers of services associated with the operation of this infrastructure. The market for horizontal/general purpose ICT solutions is also characterized by the virtual absence of technological interactions among the developers of this category of ICT solutions and agribusiness claimants. Segment products ranging from operating systems, programming languages, through productivity product software (word, spreadsheet, database processors, etc.) to personal computers, cell phones and tablets, are developed to meet demands not specific to this or any other industry, or even which are customized for each type of destination. These markets are highly concentrated, due to factors related to the significant advantages of scale associated with the lock-in effect resulting from the enforcement of dominant technological standards in an often global scale. As a result, the players who offer these technologies are usually large global corporations, with market power.

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CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

Endogenous component: interaction with the world of agribusiness is a key factor in the innovation process and in the competitive success. The ICT market related to the endogenous component, in contrast, displays a rather different setup. The need to satisfy demands and needs specific to segments of the agricultural market makes the interaction with the claimants a key factor in the innovation process and in the competitive success. In the segment that offers vertical solutions focused on niches, the competitive advantages determined by scaled gains or by the enforcement of dominant standards are smaller, resulting in fewer walls protecting the entry. In these segments, the importance of interaction and proximity to the claimants may result in greater opportunities for the operation of domestic companies, including small- and medium-sized ones. As for ICT demanded by agribusiness which are embedded in agricultural equipment and implements, the average size of companies is presumably higher, since these technologies are usually developed by R&D departments of companies of the capital goods sector, and the presence of transnational corporations is significant. A summary of the proposed typology, introducing the two major simultaneous components of expansion of use of ICT in agribusiness, which, as they operate, they build and make up the value chain, is shown in Figure 1.2. FIGURE 1.2 – VALUE CHAIN AND MARKET STRUCTURE OF ICT ACTIVITIES FOR AGRIBUSINESS

Vertical solutions focused on niches

ICT embedded in equipment/implements

Number of agents

Specifics of solutions

Overall control of the value chain by multinational

Overall ICT Infrastructure Horizontal/general purpose solutions

Influence of agribusiness’ demands in technological development of the ICT Exogenous  

Endogenous

Source: Softex Monitoring Centre, based on Diegues & Roselino (2012).

The following is a brief overview with the large agribusiness figures in Brazil and its economic relevance.

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

1.2 SECTORIAL OVERVIEW OF BRAZILIAN AGRIBUSINESS AND ITS INTERNATIONAL INSERTION In 2013, agribusiness accounted for 22% of Brazilian GDP. Brazilian agribusiness, which main activities is agriculture and livestock, accounted for approximately 22% of the Gross Domestic Product (GDP) in 20131, experiencing an average annual growth of 2.84% from 2000 to 2013 (Figure 1.3). FIGURE 1.3 – ANNUAL GDP VARIATION IN BRAZILIAN AGRIBUSINESS (2000 TO 2013) 10 8 6 4 2 0 -2

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

-4 -6 -8 Source: Morais et al (2015).

Over the years, significant productivity gains are observed. More significant than the growth indicators of the agricultural GDP are the sector's productivity gains. Gasques et al (2011) measured that the total factor productivity (TFP) of Brazilian agriculture has achieved an average annual increase of 5.31% from 2000 to 2010. This notion, related to the modernization of Brazilian agribusiness, has resulted in a reduced number of people occupied in the industry, which declined from 23.4 million in 1985 to 15.2 million in 2006, according to data from IBGE’s Agricultural Census compiled by Morais et al (2015). The Brazilian balance of trade owes its positive performance to agribusiness exports. The importance of Brazilian agribusiness is even more significant when considering the industry’s results for the balance of trade. The surplus, or virtual balance in a few years, the Brazilian commercial transactions are due to the significant positive performance of agribusiness exports.

1 From the comprehensive concept of agribusiness industry which includes, in addition to agricultural production, inputs, agroindustries and related services, according to the delimitation of scope of the industry proposed by Davis & Goldberg (1957).

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CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

It is possible to scale the importance of Brazilian agribusiness in terms of the international market based on the information presented in Table 1.1. The data show that the value of international trade related to agricultural products used to represent 7.4% of the total amount dealt in 2013. The condition of Brazil as an agricultural power is expressed in a participation in the international trade of agricultural products proportionally much higher than the relative size of the Brazilian economy and its importance in international trade in general. Brazil participates in the overall international trade accounting for about 1.7% of the figures dealt. When looking at the figures related to the Brazilian participation in the international trade of agricultural products, the Brazilian importance is significantly higher, accounting for a growing share in the period exposed until reaching 7.6% in 2013. TABLE 1.1 – OVERALL INTERNATIONAL AND AGRICULTURAL TRADE AND BRAZILIAN PARTICIPATION – 2004 TO 2013 In US$ Billions

 

2004

2005

World Total (A)

7.012,68

8.090,30

World Agric. (B)

485,9

526,23

575,86

697,54

857,7

756,48

878,42

1.093,34

1.112,85

1.141,07

6,9%

6,5%

6,1%

6,5%

6,8%

7,7%

7,1%

7,4%

7,4%

7,4%

Brazil Total (C )

96,68

118,53

137,81

160,65

197,94

152,99

197,36

256,04

242,58

242,18

Brazil Agric. (D)

28,36

32,21

36,94

44,89

58,36

54,83

63,68

81,8

83,41

86,64

D/C

29,3%

27,2%

26,8%

27,9%

29,5%

35,8%

32,3%

31,9%

34,4%

35,8%

C/A

1,4%

1,5%

1,5%

1,5%

1,6%

1,6%

1,6%

1,7%

1,6%

1,6%

D/B

5,8%

6,1%

6,4%

6,4%

6,8%

7,2%

7,2%

7,5%

7,5%

7,6%

B/A

2006

2007

2008

9.373,73 10.707,94 12.647,62

2009

2010

2011

2012

2013

9.851,91 12.288,87 14.797,70 15.127,50 15.359,80

Includes products in annex 1 of the Agreement on Agriculture of WTO-1994, and fish; Excluding EU-28 intratrade. Source: MAPA (2014).

Table 1.2, in turn, shows data related to the participation of agribusiness in the Brazilian foreign trade. The data point out that the agribusiness has a significant and structural surplus for decades, and is individually responsible for a substantial part of the inflow of foreign currency to the Brazilian economy. The data also reveal the significant jump in the surplus balance related to the Brazilian agribusiness since the beginning of the 2000s. The sectoral surplus jumps from something close to US$ 15 billion in 2000 to approximately US$ 80 billion in recent years. This movement was caused not only due to the expansion of the values exported by the sector, but also due to the relative reduction in imports. The latter years of the series presented show that exports of agribusiness accounted for more than 40% of total Brazilian exports.

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

TABLE 1.2 – BRAZILIAN BALANCE OF TRADE AND AGRIBUSINESS BALANCE OF TRADE – 1989 TO 2015 In US$ Billions

Exports Year

Total Brazil (A)

Agribusiness (B)

Imports Share% (B/A)

Total Brazil Agribusiness (C) (D)

Balance Share% (D/C)

Total Brazil

Agribusiness

1989

34,383

13,921

40,49

18,263

3,081

16,87

16,119

10,840

1990

31,414

12,990

41,35

20,661

3,184

15,41

10,752

9,806

1991

31,620

12,403

39,23

21,040

3,642

17,31

10,580

8,761

1992

35,793

14,455

40,38

20,554

2,962

14,41

15,239

11,492

1993

38,555

15,940

41,34

25,256

4,157

16,46

13,299

11,783

1994

43,545

19,105

43,87

33,079

5,678

17,16

10,466

13,427

1995

46,506

20,871

44,88

49,972

8,613

17,24

-3,466

12,258

1996

47,747

21,145

44,29

53,346

8,939

16,76

-5,599

12,206

1997

52,994

23,376

44,11

59,747

8,197

13,72

-6,753

15,178

1998

51,140

21,555

42,15

57,763

8,045

13,93

-6,624

13,511

1999

48,013

20,501

42,70

49,302

5,697

11,56

-1,289

14,804

2000

55,119

20,605

37,38

55,851

5,759

10,31

-0,732

14,845

2001

58,287

23,866

40,95

55,602

4,805

8,64

2,685

19,061

2002

60,439

24,846

41,11

47,243

4,452

9,42

13,196

20,394

2003

73,203

30,653

41,87

48,326

4,750

9,83

24,878

25,903

2004

96,677

39,035

40,38

62,836

4,836

7,70

33,842

34,200

2005

118,529

43,623

36,80

73,600

5,112

6,95

44,929

38,511

2006

137,807

49,471

35,90

91,351

6,699

7,33

46,457

42,772

2007

160,649

58,431

36,37

120,617

8,732

7,24

40,032

49,699

2008

197,942

71,837

36,29

172,985

11,881

6,87

24,958

59,957

2009

152,995

64,786

42,34

127,722

9,900

7,75

25,272

54,885

2010

201,915

76,442

37,86

181,768

13,399

7,37

20,147

63,043

2011

256,040

94,968

37,09

226,247

17,508

7,74

29,793

77,460

2012

242,578

95,814

39,50

223,183

16,409

7,35

19,395

79,405

2013

242,034

99,968

41,30

239,748

17,061

7,12

2,286

82,907

2014

225,101

96,748

42,98

229,154

16,614

7,25

-4,054

80,134

2015

191,134

88,224

46,16

171,449

13,073

7,63

19,685

75,151

Source: Agrostat Brasil from data from SECEX/MDIC. Preparation: DAC/SRI/MAPA.

Observing the composition of the top ten products listed in Brazilian exports in 2015, we see the predominance of land-related products and natural resources. Table 1.3 shows that eight of the ten products with higher values exported by Brazil in 2015 were from the Agribusiness. The eight agricultural products with more expressive values exported amounted to US$ 55.63 billion in external revenue, representing almost a third of the country’s exports that year.

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TABLE 1.3 – KEY PRODUCTS IN BRAZILIAN EXPORTS – 2015 In US$ Billions

Description 1. Soy beans 2.Gross Petroleum oils 3. Iron ores and their concentrates, with some exceptions 4. Other cane sugars 5. Green coffee beans 6. Chemical paste from non-coniferous wood 7. Oil cake and other waste from soy oil extraction 8. Corn beans 9. Frozen chicken cuts and giblets 10. Frozen and boneless meat of bovine Eight largest in the agribusiness All other agribusiness products Two largest non-agricultural products All other non-agricultural products Total Exports

Value 20,98 11,78 10,38 5,90 5,56 5,34 5,00 4,93 3,96 3,95 55,63 32,59 22,16 80,78 191,13

Share 10,98% 6,16% 5,43% 3,09% 2,91% 2,80% 2,62% 2,58% 2,07% 2,07% 29,11% 17,05% 11,59% 42,26% 100%

Source: CNA (2016).

Brazilian agribusiness is essential to the balance of the Brazilian balance of payments, but focuses in a few products and export destinations. As seen, Brazilian exports are widely focused on just a few agricultural products. This focus also exists with regard to the geographical origin of these exports. In 2015, only four states (São Paulo, Mato Grosso, Rio Grande do Sul & Minas Gerais) accounted for approximately two-thirds of the amount exported (Figure 1.4)2. FIGURE 1.4 – AGRIBUSINESS EXPORTS PER STATE OF ORIGIN – BRAZIL, 2015 In US$ Billions

São Paulo, 15,88, 21% All Other States 28.83, 38% Mato Grosso, 12,93, 17%

Minas Gerais, 7,31, 9%

Rio Grande do Sul, 11,63, 15%

Source: CNA (2016).

2 It is important to point out that the trade data per origin of exports is not an accurate indicator of agribusiness production origin (or of any other commodity), since it shows the origin of the record of the company responsible for the export, and not the origin of the production process.

18

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

The target markets for Brazilian exports of agribusiness products are also few (Figure 1.5). China occupies a prominent place as claimant of products exported by Brazilian agribusiness, individually absorbing 24% of these exports. Chinese demand is particularly important for soybeans, and they account for about 75% of total exports of the commodity in 2015 (CNA, 2016). FIGURE 1.5 – AGRIBUSINESS EXPORTS PER DESTINATION – 2015 In US$ Billions

China, 21,28, 24% All Other Destinations, 38.41, 43% European Union, 18.26, 21% United States, MERCOSUR, 6,47, 7% 4,07, 5%

Source: CNA (2016)

In the section below, from our very own methodological approach, we intend to move forward in identifying opportunities and challenges for the adoption of ICT in Brazilian agribusiness.

1.3 THE BRAZILIAN ICT MARKET FOR AGRIBUSINESS This section shows, at first, the definition of the scope and the methodology used to map the Brazilian market for software and ICT services focused on agribusiness, and then, provide the results of this mapping.

DEFINITION OF THE SCOPE In general, the cutout given in the literature to the agribusiness sector includes economic activities of a horizontal nature spread over a wide range of sectors. This is the case, for example, of activities of manufacturing machinery and equipment; of transport services and wholesale trade. These activities play an important role in the agribusiness production chain, but they are not totally and exclusively addressed to the sector’s needs. Thus, adopting a wide definition of scope of agribusiness could lead to an overestimation of ICT adoption by the sector. In order to overcome this limitation, we chose to use a strict agribusiness concept, taking into account only its key activities: agriculture and agricultural industry. Concerning agriculture, we took into consideration the activities classified in Section A of the National Classification of Economic Activities (NCEA), version 2.0, which includes three divisions: agriculture, livestock and related services; forestry production; fishing and aquaculture. Concerning the agricultural industry, we took into consideration the activities of manufacturing of foodstuffs, beverages and tobacco products.

19

CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

METHODOLOGY Instead of gauging the degree of agribusiness computerization by the revenue from the sale of ICT products and services for the sector, this work indirectly estimates the actual and potential demand of the sector by the level of presence of ICT professionals in it. The procedure is based on the following premise: adoption of ICT in a given establishment or undertaking requires the hiring of ICT professionals to perform various internal activities to support the computerization process, which may include, for example, the specification of software to be ordered to third parties; the development of own solutions; the integration and maintenance of purchased solutions; the provision of technical support to end users in the use of computerized systems available; the insertion of data and information in databases, etc. Thus, based on the guidelines proposed by the methodology, the intention was to identify the presence of professionals formally employed in occupations dealing with ICT (PROFTIC) in agribusiness3. For the definition of occupations in ICT, we analyzed the set of activities carried out by professionals from 596 occupational families listed in the Brazilian Classification of Occupations (CBO). Seventeen of them were selected because they had activities directly related to the development of products or the provision of ICT services (Box 1.2). BOX 1.2 – OCCUPATIONAL FAMILIES PERFORMING ACTIVITIES RELATING TO INFORMATION AND COMMUNICATION TECHNOLOGIES CODE IN CBO 1236 1425 2021 2122 2123 2124 2032 2143 2612 3001 3003 3132 3171 3187 3722 9502 9531

OCCUPATIONAL FAMILY Directors of IT services Information technology managers Mechatronic engineers IT engineers IT Experts Computer systems analyst Engineering and technology researchers Electro-electronic engineers and the like Information professionals Mechatronics technicians Electro-mechanical technicians Electronic technicians Programming technicians Electronic project designers Technicians in operation of data transmission machinery Supervisors of electro-electronic maintenance of vehicles Electronic electricians of maintenance of (air, ground and marine) vehicles

Source: Softex Monitoring Centre, based on the Brazilian Classification of Occupations, 2002 version.

3 Details concerning the methodology may be found in Softex Monitoring Centre, 2009, and also in Diegues & Roselino, 2011.

20

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

One caveat to the methodology: the adoption of ICT in agribusiness may be occurring without the need of hiring ICT professionals. As one of the great advantages of this methodology, extensively validated in numerous sectors, as may be seen in the studies mentioned previously, the fact that it is able to estimate the demand according to a high level of disaggregation should be highlighted. Thus, it would be possible to adopt sectoral cutouts, geographical cutouts, cutouts by size and type of company, among countless other possibilities. However, it is worth pointing out that this validity is so much greater the greater the degree of complexity of the ICT solutions adopted. We explain: in a given sector in which highly complex solutions are demanded at the same time in which in-house solutions (such as for the financial department, for example) are developed, we see a greater correlation between the PROFTIC number and the potential demand (estimated at around 0.98 by Diegues & Roselino, 2010). It is likely, however, that in sectors in which the degree of complexity of ICT solutions adopted is low, the correlation is lower. Thus, due to the type of products, services and ICT solutions used today in Brazilian agriculture and agricultural industry, it is possible that the sector may waive the hiring of PROFTIC, which usually does not occur in other economic sectors.

MAPPING RESULTS The participation of PROFTIC in agribusiness is low: less than 2% of the total. Its presence in the agricultural industry is about four times higher than in agriculture. A first observation concerns the reduced participation of PROFTIC in agribusiness. As shown in Figure 1.6, from 2010 to 2014, the number of PROFTIC in the sector accounted for 1.67% to 1.84% of the total. FIGURE 1.6 – PROFTIC IN AGRIBUSINESS IN RELATION TO TOTAL PROFTIC – BRAZIL, 2010 TO 2014 In % 1,90% 1,85% 1,80% 1,75% 1,70% 1,65% 1,60% 1,55% 2010

2011

2012

2013

2014

Source: Softex Monitoring Centre, based on data from the Annual List of Social Information (RAIS)/MTE.

21

CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

The participation of PROFTIC in the agricultural industry is about four times higher than in agriculture (10,477 and 2,568 professionals, respectively, in 2014). Although from 2010 to 2014 both segments of the agribusiness having seen a substantial growth in the number of PROFTIC (19% and 29%, respectively), from 2013 to 2014, a drop in the quantity employed in agriculture was noticed (figures 1.7 & 1.8). FIGURE 1.7 – EVOLUTION OF THE QUANTITY OF PROFTIC IN THE AGRICULTURAL INDUSTRY – BRAZIL, 2010 TO 2014

11.000 10.500 10.000 9.500 9.000 8.500 8.000 2010

2011

2012

2013

2014

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

FIGURE 1.8 – EVOLUTION OF THE QUANTITY OF PROFTIC IN AGRICULTURE – BRAZIL, 2010 TO 2014 2.800 2.700 2.600 2.500 2.400 2.300 2.200 2.100 2.000 1.900 1.800 2010

2011

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

22

2012

2013

2014

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

A high concentration of PROFTIC is seen in five Brazilian states: SP, MG, PR, RS and GO. In the agricultural industry, the level of geographical concentration is relatively higher. Despite the big difference between the magnitude of PROFTIC in agriculture and in the agricultural industry, when considering their geographic distribution, similar trends are noted: there is a high concentration in five among the top states, especially, in both cases, in São Paulo, Minas Gerais, Paraná, Rio Grande do Sul and Goiás. In the agricultural industry, the level of geographical concentration is relatively higher, which may be explained at least partly by the historical roots of industrial concentration in São Paulo, as well as by the nature geographically more scattered (given the importance of natural determinants) of the agricultural activities when compared to the agricultural industry (in view of the greater degree of industrial processing present in this sector). These characteristics may be seen in figures 1.9 and 1.10. FIGURE 1.9 – STATE-WISE PROFTIC DISTRIBUTION IN THE AGRICULTURAL INDUSTRY – BRAZIL, 2014 PROFTIC Total: 10,477

Others 26% São Paulo 41% Goiás 5% Rio Grande do Sul 7%

Paraná 9%

Minas Gerais 12%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

FIGURE 1.10 – STATE-WISE PROFTIC DISTRIBUTION IN AGRICULTURE – BRAZIL, 2014 PROFTIC Total: 2,568

Others 24%

São Paulo 33%

Rio Grande do Sul 4% Mato Grosso do Sul 6%

Goiás 7%

Mato Grosso 9%

Minas Gerais 12%

Paraná 5%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

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CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

Both in the agricultural industry and in agriculture, the PROFTIC focus on the activities that make up the sugarsugarcane-producing complex. The concentration also happens in the activities of the sector. Both in the agricultural industry and in agriculture, a major highlight is observed for the activities that make up the sugar-sugarcane-producing complex. In this regard, however, the previously described phenomenon is reversed, since the degree of PROFTIC concentration is higher in agriculture (Figures 1.11 and 1.12). Some factors may explain this trend, namely: (i) the historical roots of the concentration process of agricultural activities in a limited set of activities related to the dependent and reflex insertion into the international division of labor and (ii) the very intrinsic specifics of the agricultural and industrial activities, which allow for greater diversification in their productive fields. FIGURE 1.11 – SEGMENT-WISE PROFTIC DISTRIBUTION IN THE AGRICULTURAL INDUSTRY – BRAZIL, 2014 PROFTIC Total: 10,477

Others 37%

Raw sugar production 27%

Dairy production 8% Production of foodstuffs not previously specified 5%

Slaughter of pigs, birds and other small animals 6%

Production of soft drinks and other non-alcoholic beverages 5%

Slaughter of cattle, except 5%

Production of malt, beer and draft beer 6%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

FIGURE 1.12 – SEGMENT-WISE PROFTIC DISTRIBUTION IN AGRICULTURE – BRAZIL, 2014 PROFTIC Total: 2,568

Others 25% Sugarcane crops 37%

Cereal crops 4% Cattle farming 5% Agriculturalsupporting activities 9%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

24

Poultry 10%

Soybean crops 10%

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

ICT penetration in agribusiness is still highly focused on large-sized companies. As for the agricultural industry, 90% of the PROFTIC total focus on companies with 100 or more employments (71% for agriculture). One aspect worth mentioning is that, despite the recent movement of expansion of ICT activities associated with agribusiness and despite the reconfiguration trend of the very ICT solutions towards business models with the possibility of reducing fixed costs to be carried out by the claimants through the sale of services on demand, if measured by the presence of PROFTIC, ICT penetration in agribusiness is still highly focused on large-sized companies. In the agricultural industry, 90% of the PROFTIC total focus on companies with 100 or more employments, from which 43% are in companies with 1,000 or more employments. Percentages for agriculture are 71% and 33%, respectively (Figures 1.13 and 1.14). FIGURE 1.13 – BUSINESS SIZE-WISE PROFTIC DISTRIBUTION IN THE AGRICULTURAL INDUSTRY – BRAZIL, 2014 PROFTIC Total: 10,477 Less than 100 10% 100-249 12% 1,000 or More 43% 250-499 15%

500-999 20%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

FIGURE 1.14 – BUSINESS SIZE-WISE PROFTIC DISTRIBUTION IN AGRICULTURE – BRAZIL, 2014 PROFTIC Total: 2,568

Less than 50 20% 1,000 or More 33% 50-99 9%

100-249 14%

500-999 13% 250-499 11%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

25

CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

Over 20% of ICT demand applied to the agricultural industry and agriculture are focused on companies with 1,000 or more employments in the sugar-producing complex. The intersection between business size and economic activities allows the estimation of the top user markets and potential ICT claimants, both in agriculture and in the agricultural industry. Thus, based on the review of attached tables 1.A1 and 1.A2, and considering the presence of PROFTIC as a relevant indicator of the adoption of ICT, it is emphasized that: a. 21% of the potential demand for ICT solutions applied to the agricultural industry is focused on businesses with 1,000 or more employments belonging to the raw sugar producing segment. The significant remaining part focuses on businesses with 1,000 or more employments with activities of slaughter of pigs, birds and other small animals (3.9%) and dairy production (3.7%), and in businesses having 500-999 employments belonging to the segment of production of malt, beer and draft beer (3.8% of the total). b. 22% of the potential demand for ICT solutions applied to agriculture is focused on businesses with 1,000 or more employments in the sugarcane crops segment (8% among businesses with 500-999 employments and 4% among businesses with 250-499 employments), 4% among businesses with 1,000 or more employments in the segment of agriculture-supporting activities and 3% in the soybeans crops segment with 100-249 employments. Finally, in order to estimate more precisely such a market, it is worth submitting some observations concerning its distribution according to the meso-regions. The top twenty meso-regions account for 2/3 of the national ICT market for agribusiness: there is emphasis for metro areas and areas with thriving agribusinesses. Concerning the agricultural industry (Table 1.4), it is observed that the top twenty meso-regions account for 2/3 of the domestic market. Two market patterns are basically identified: those associated with major metro areas and those present in areas traditionally known for its strength in agribusiness, such as the countryside of São Paulo, Paraná, Minas Gerais and Goiás. TABLE 1.4 – DISTRIBUTION OF PROFTIC EMPLOYED IN THE AGRICULTURAL INDUSTRY, ACCORDING TO THE MESOREGION OF LOCATION OF THE BUSINESS – BRAZIL, 2014 PROFTIC Total: 10,477

MESO-REGION São Paulo Metro Area Ribeirão Preto Minas Gerais’ Triangle/High Paranaíba Piracicaba São José do Rio Preto Campinas Rio de Janeiro Metro Area Downtown Goiás Belo Horizonte Metro Area

26

% 14% 7% 5% 4% 3% 3% 3% 3% 3%

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

Northern Central Paraná Eastern Alagoas Macro São Paulo Metro Area Fortaleza Metro Area Southern Goiás Porto Alegre Metro Area Eastern Central Rio Grande do Sul Northwestern Paraná Southern/Southwestern Minas Gerais Recife Metro Area Curitiba Metro Area

3% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

With regard to agriculture (Table 1.5), a small reduction in the overall concentration trend and in the participation of metro areas is observed. In addition, the presence of meso-regions located in the states of Mato Grosso and Mato Grosso do Sul is expanded, while the participation of those located in Minas Gerais is reduced. TABLE 1.5 – DISTRIBUTION OF PROFTIC EMPLOYED IN AGRICULTURE, ACCORDING TO THE MESO-REGION OF LOCATION OF THE BUSINESS – BRAZIL, 2014 PROFTIC Total: 2,568

MESO-REGION São José do Rio Preto Assis Ribeirão Preto Brazilian Federal District Southern Goiás Minas Gerais’ Triangle/High Paranaíba Campinas Northern Mato Grosso Southeastern Mato Grosso Belo Horizonte Metro Area São Paulo Metro Area Eastern Mato Grosso do Sul Bauru Southwestern Mato Grosso do Sul Downtown Goiás Northeastern Pará Southwestern Mato Grosso Pioneer Northern Paraná Northern Central Mato Grosso do Sul Far Western Bahia Others

% 8% 7% 6% 5% 5% 4% 3% 3% 3% 2% 2% 2% 2% 2% 2% 2% 2% 1% 1% 1% 37%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

27

CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

It is also worth mentioning, as a complement to the information in this work, that it is also possible to segment by regions the separation of markets according to segment types. Thus, attached tables 1.A3 and 1.A4 allow the estimation, respectively for the agricultural industry and agriculture, which is the participation of each of the 107 meso-regions in each of the 58 segments. Thus, one could estimate, for example, the participation of the Ribeirão Preto meso-region in each of the 58 agribusiness segments. Conversely, it can be seen how the PROFTIC of segment cereal crops, for example, are distributed in each of the 107 Brazilian meso-regions. That is, these arrays allow the estimation of the demand for ICT in 6,206 market segmentations of agribusiness (in which economic activities are analyzed by region).

FINAL THOUGHTS DEVELOPMENT OPPORTUNITIES FOR SOFTWARE AND ICT SERVICES ACTIVITIES FOR AGRIBUSINESS In order to analyze the development opportunities for software and ICT services activities focused on agribusiness, this work uses a traditional instrument, the SWOT matrix. This, based on the segmentation in internal components (strengths and weaknesses) and external (opportunities and threats), systematize the factors influencing the development possibilities of a particular activity. Among the top forces favorable to ICT activities in the Brazilian agribusiness, there is the high level of strength of the claimant sector, namely the agribusiness. This sector historically characterizes itself as one of the central axes of the development of Brazilian economy, and is marked by the presence of a profitable business structure, linked to global production chains and with a high power of investment (Box 1.3). This strength, coupled with the presence of historical technological skills in developing agribusiness solutions (many of them invariably associated with public institutions such as Embrapa and various other institutes) and with the prominent position of the Brazilian agribusiness in the international scene provide a high potential of demand for ICT solutions. This is reinforced by the high degree of mechanization of agribusiness segments geared to the international market. Last but not least, in addition to the strength in the claimant sector, their strength in the capacity of domestic supply of software and ICT services should be highlighted. The local industry is rather vibrant, they are among the top ten in the world, and associate with a diverse domestic production structure and with technological skills well developed in several segments. Still concerning the internal component of the SWOT matrix, some elements (weakness) are also shown in parallel to the scenario described in the preceding paragraphs. Thus, it should be noted that the possibilities of development of the activities in question in this work are roughly and negatively influenced by the relatively low level of application of ICT in the Brazilian agribusiness. Thus, despite the possibilities already mentioned, it is noted that the degree of computerization of these activities is still quite limited, as well as the availability of a basic ICT infrastructure in rural areas of Brazil. In addition, it should be emphasized that, in the agribusiness segments not associated with the international market, the level of profitability is generally low, which reduces their investment capability. Added to this, is the low level of qualification of labor and the still very rudimentary business management techniques. Finally, it is recorded that, in cases of embedded software in machinery and equipment, the solutions is invariably developed and commercialized by transnational companies that dominate globally the agricultural equipment sector.

28

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

Despite these determinants, the analysis of the external component of the SWOT matrix allows the identification of a wide range of opportunities of development of software and ICT services activities applied to agribusiness. In general, they are associated with three vectors: c. The strength of Brazilian agribusiness (consolidation of Brazilian agribusiness as the world center, persistence, at least for an average term, of a reasonably sustained cycle of international demand for commodities). d. The technological changes in ICT (integration to other local production chains through the vetting, in part, of technologies associated with the Internet of Things; reducing costs of solutions associated with customization; expansion of ICT infrastructure). e. The changes in the competitive and innovative dynamics, as well as the dynamics of environmental regulations (substantially growing need to ensure environmental sustainability in production practices, adequacy of agribusiness to the demands of international markets concerning handling, traceability and production techniques). However, the consolidation of these opportunities has, as a determinant, the at least partial overcoming of a non-negligible number of threats. With regard to the domestic economy, there is the recent scenario of drop of the GDP and the environmentally degrading manner that has been the historical driving force for the expansion of Brazilian agribusiness, including the movement of expansion of agricultural borders started in the 60s. As far as the international economy is concerned, some threats that deserve emphasizing are the historical swings in international commodity prices and the intensification of competitive pressures in a scenario of semi-stagnation of the global economy. BOX 1.3 – SWOT REVIEW: BRAZILIAN MARKET FOR SOFTWARE AND ICT SERVICES FOR AGRIBUSINESS

STRENGTHS

EXTERNAL

INTERNAL

High agribusiness dynamics. Presence of historical expertise in the software industry.

WEAKNESSES Low computerization level. Deficient basic telecommunications infrastructure.

Prominent positioning of Brazilian agribusiness in the international scene.

Low level of labor qualification.

Technological and scientific prominence of Brazilian agribusiness.

Low profitability / investment capacity in segments not coupled to the international market.

High degree of mechanization in the agribusiness segments focused on the international market.

Machinery and equipment supply chain dominated by multinational companies, which already offer embedded solutions.

Existence of a profitable business structure, linked to the global production chains.

Great fragmenting of the ICT solutions offering market.

29

CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

OPPORTUNITIES

THREATS

INTERNAL

Consolidation of Brazilian agribusiness as the world center. Persistence, at least for an average term, of a reasonably sustained cycle of international demand for commodities.

Swing in international commodity prices. GDP drop scene.

Adequacy of agribusiness to the demands of international markets concerning handling, traceability and production techniques.

EXTERNAL

Reduced costs of solutions associated with customization. Intensification of international competitive pressures.

Expansion of ICT infrastructure. Integration to other local production chains through the vetting, in part, of technologies associated with the Internet of Things. Substantially growing need to ensure environmental sustainability in production practices.

Continued expansion of Brazilian agribusiness in parallel to the adoption of cropping techniques degrading to the environment.

TABLE 1.A1 – SEGMENT AND BUSINESS SIZE-WISE DISTRIBUTION OF PROFTIC IN THE AGRICULTURAL INDUSTRY – BRAZIL, 2014 PROFTIC Total: 10,477 SEGMENT (CNAE 2.0 Class) Slaughter of cattle, except pigs Slaughter of pigs, birds and other small animals Production of meat products Preservation of fish and production of fish Production of canned fruit Production of canned vegetables and other vegetables Production of fruit juices, vegetables and legumes Production of raw vegetable oils, except maize oil Production of refined vegetable oils, except maize oil Production of margarine and other vegetable fats and of non-edible animal oils Preparation of milk Dairy production Production of ice cream and other edible iced products Rice processing and production of rice products Wheat milling and production of by-products Production of manioc flour and by-products Production of corn flour and by-products, except maize oils Production of starches and vegetable starches and maize oils Production of animal food Milling and production of plant products not previously specified Production of raw sugar Production of refined sugar Coffee roasting and grinding Production of coffee-based products Production of bakery products Production of cookies and crackers Production of cocoa by-products, chocolate and fondant Production of pasta Production of spices, sauces, dressings and condiments

30

1-4 0% 0% 0% 0% 0% 0% 0% 0% 0%

5-9 0% 0% 0% 0% 0% 0% 0% 0% 0%

10-19 0% 0% 0% 0% 0% 0% 0% 0% 0%

20-49 0% 0% 0% 0% 0% 0% 0% 0% 0%

BUSINESS SIZE (employments) 50-99 100-249 250-499 0% 0% 1% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 1% 0% 0% 0%

500-999 1% 1% 0% 0% 0% 0% 0% 0% 0%

1000 or More 2,9% 3,9% 0,3% 0,1% 0,0% 0,2% 0,2% 0,0% 0,8%

Total 5% 6% 1% 1% 0% 0% 2% 2% 1%

0%

0%

0%

0%

0%

0%

0%

1%

0,0%

1%

0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0%

0% 1% 0% 0% 0% 0%

0% 1% 0% 0% 0% 0%

0% 2% 0% 0% 1% 0%

0% 1% 0% 0% 0% 0%

0,0% 3,7% 0% 0% 0% 0%

1% 8% 1% 1% 2% 0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

1%

0%

0%

0%

0%

1%

1%

1%

0%

0%

3%

0%

0%

0%

0%

0%

0%

0%

0%

0%

1%

0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0%

1% 0% 0% 0% 0% 0%

1% 0% 0% 0% 0% 0%

1% 0% 0% 0% 1% 0%

3% 0% 1% 0% 0% 0%

21% 0% 0% 0% 0% 1%

27% 1% 1% 1% 2% 2%

0%

0%

0%

0%

0%

0%

0%

1%

1%

3%

0% 0%

0% 0%

0% 0%

0% 0%

0% 0%

0% 0%

0% 0%

1% 0%

1% 0%

2% 0%

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

SEGMENT (CNAE 2.0 Class) Production of food and entrees Production of foodstuffs not previously specified Production of brandies and other spirits Production of wine Production of malt, beer and draft beer Production of bottled water Production of soft drinks and other non-alcoholic beverages Industrial processing of tobacco Production of tobacco products TOTAL

1-4 0% 0% 0% 0% 0% 0%

5-9 0% 0% 0% 0% 0% 0%

10-19 0% 0% 0% 0% 0% 0%

20-49 0% 0% 0% 0% 0% 0%

BUSINESS SIZE (employments) 50-99 100-249 250-499 0% 0% 0% 1% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0%

500-999 0% 1% 0% 0% 3,8% 0%

1000 or More 0% 1% 0% 0% 1% 0%

Total 1% 5% 1% 0% 6% 0%

0%

0%

0%

0%

0%

1%

1%

1%

2%

5%

0% 0% 0%

0% 0% 0%

0% 0% 1%

0% 0% 3%

0% 0% 6%

0% 0% 12%

0% 1% 16%

0% 1% 20%

0% 1% 43%

1% 3% 100%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

TABLE 1.A2 – SEGMENT AND BUSINESS SIZE-WISE DISTRIBUTION OF PROFTIC IN AGRICULTURE – BRAZIL, 2014 PROFTIC Total: 2,568 SEGMENT (CNAE 2.0) Cereal crops Crops of upland cotton and other seasonal crop fibers Sugarcane crops Tobacco crops Soybean crops Seasonal crops of oleaginous plants, except soybean Seasonal crops of plants not previously specified Horticulture Cultivation of flowers and ornamental plants Orange crops Grape crops Cultivation of fruits of permanent crops, except oranges and grapes Coffee crops Cocoa crops Cultivation of plants of permanent crops not previously specified Production of certified seeds Production of seedlings and other certified forms of plant propagation Cattle farming Farming of other large-sized animals Swine breeding Poultry breeding Agricultural-supporting activities Livestock-supporting activities Post-harvesting activities Forest production - planted forests Forest production - natural forests Forest production-supporting activities Saltwater fishing Aquaculture in saltwater and brackish water Aquaculture in fresh water TOTAL

1-4 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

5-9 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

10-19 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0%

20-49 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0%

BUSINESS SIZE (employments) 50-99 100-249 250-499 0% 0% 0% 1% 0% 0% 1% 2% 4% 0% 0% 0% 1% 3% 1% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

500-999 0% 0% 8% 0% 1% 0% 0% 0% 0% 0% 0%

1000 or More 0% 0% 22% 0% 1% 0% 0% 0% 0% 0% 0%

TOTAL 5% 1% 37% 0% 10% 0% 2% 1% 0% 1% 0%

0%

0%

0%

0%

0%

0%

0%

1%

0%

2%

0% 0%

0% 0%

0% 0%

0% 0%

0% 0%

0% 0%

0% 0%

0% 0%

0% 0%

1% 0%

0%

0%

0%

0%

0%

0%

0%

0%

1%

2%

0%

0%

0%

0%

0%

0%

1%

0%

0%

2%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4%

1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 5%

1% 0% 0% 1% 1% 0% 0% 0% 0% 1% 0% 0% 0% 9%

1% 0% 0% 2% 1% 0% 0% 1% 0% 1% 0% 0% 0% 9%

1% 0% 0% 2% 0% 0% 0% 0% 0% 1% 0% 0% 0% 14%

0% 0% 0% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 11%

0% 0% 0% 1% 0% 1% 0% 1% 0% 0% 0% 0% 0% 13%

0% 0% 0% 2% 4% 0% 0% 0% 0% 1% 0% 0% 0% 33%

5% 0% 1% 10% 9% 1% 1% 3% 0% 4% 0% 1% 0% 100%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

31

CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

TABLE 1.A3 – SEGMENT-WISE AND MESO-REGION-WISE DISTRIBUTION OF PROFTIC IN THE AGRICULTURAL INDUSTRY – BRAZIL, 2014

32

Production of meat products

Preservation of fish and production of fish products

Production of canned fruit

Production of canned vegetables and other vegetables

Production of fruit juices, vegetables and legumes

Production of raw vegetable oils, except maize oil

Production of refined vegetable oils, except maize oil

Production of margarine and other vegetable fats and of non-edible animal oils

Preparation of milk

Dairy production

Production of ice cream and other edible iced products

Rice processing and production of rice products

Wheat milling and production of by-products

Production of manioc flour and by-products

Production of corn flour and by-products, except maize oils

São Paulo Metro Area Ribeirão Preto Minas Gerais Triangle/High Paranaíba Piracicaba São José do Rio Preto Campinas Rio de Janeiro Metro Area Downtown Goiás Belo Horizonte Metro Area Northern Central Paraná Eastern Alagoas Macro São Paulo Metro Area Fortaleza Metro Area Southern Goiás Porto Alegre Metro Area Eastern Central Rio Grande do Sul Northwestern Paraná Southern/Southwestern Minas Recife Metro Area Curitiba Metro Area Pernambuco Woods Araraquara Araçatuba Southwestern Mato Grosso do Sul Itapetininga Bauru Western Santa Catarina Western Paraná Presidente Prudente Northwestern Rio Grande do Sul Vale do Paraíba in São Paulo Assis Brazilian Federal District Marília Eastern Central Paraná Zona da Mata Salvador Metro Area Northeastern Rio Grande do Sul Paraíba Woods Belém Metro Area Vale do Itajaí Southern Central Mato Grosso Downtown Amazonas Eastern Sergipe Northern Mato Grosso Serrana Northern Santa Catarina Downtown Minas Gerais Pioneer Northern Paraná Eastern Rio Grande do Norte Southwestern Paraná Southeastern Mato-grosso Southwestern Rio Grande do Sul Vale do Rio Doce Eastern Rondônia Western Central Rio Grande do Sul Campo das Vertentes Southern Santa Catarina Southern Rio de Janeiro State

Slaughter of pigs, birds and other small animals

MESO-REGION

Slaughter of cattle, except pigs

PROFTIC Total: 10,477

35% 6%

1% 0%

22% 3%

15% 4%

3% 26%

11% 0%

3% 8%

18% 1%

43% 16%

36% 0%

0% 13%

44% 0%

7% 0%

3% 0%

28% 0%

0% 0%

8% 0%

3%

0%

0%

0%

0%

0%

4%

0%

1%

12%

3%

8%

1%

2%

1%

0%

0%

0% 1% 0% 0% 1% 4% 1% 0% 0% 0% 1% 0% 0% 2% 1% 0% 0% 0% 0% 0% 1% 0% 3% 0% 0% 2% 0% 0% 0% 1% 0% 0% 0% 1% 0% 0% 1% 3% 3% 0% 0% 5% 0% 0% 0% 0% 0% 0% 2% 1% 1% 2% 1% 0% 0% 0%

1% 1% 1% 1% 1% 2% 10% 0% 3% 0% 4% 8% 5% 3% 1% 0% 1% 0% 1% 0% 1% 1% 1% 9% 9% 1% 6% 0% 0% 1% 0% 2% 2% 0% 5% 0% 0% 1% 0% 0% 0% 2% 0% 0% 0% 1% 0% 5% 0% 0% 0% 0% 0% 3% 0% 0%

0% 1% 5% 5% 1% 0% 1% 0% 6% 1% 0% 1% 3% 1% 0% 1% 3% 5% 0% 2% 0% 1% 5% 3% 1% 0% 9% 0% 0% 0% 1% 0% 0% 2% 0% 0% 0% 0% 1% 0% 0% 0% 1% 0% 0% 0% 1% 2% 0% 8% 0% 0% 0% 0% 0% 0%

6% 0% 0% 2% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 2% 0% 0% 2% 0% 2% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 4% 39% 0% 4% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 5% 5% 5% 0% 0% 0% 8% 5% 0% 3% 0% 0% 3% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 8% 0% 0% 0% 0% 0% 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 2% 60% 0% 0% 0% 6% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

2% 0% 3% 1% 2% 0% 0% 0% 1% 3% 0% 0% 0% 1% 0% 5% 0% 0% 35% 0% 0% 0% 0% 1% 0% 0% 0% 13% 1% 0% 0% 0% 4% 0% 1% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0%

0% 3% 0% 0% 5% 0% 1% 0% 1% 0% 7% 5% 3% 0% 0% 1% 0% 0% 0% 1% 0% 0% 0% 12% 5% 3% 4% 0% 0% 0% 0% 2% 0% 0% 1% 0% 3% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 0%

1% 0% 0% 0% 0% 0% 3% 0% 0% 0% 22% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 1% 0% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 1% 0% 0% 0% 0%

0% 0% 37% 0% 0% 0% 0% 0% 0% 8% 0% 0% 0% 0% 0% 0% 7% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 15% 0% 0% 5% 0% 0% 2% 0% 0% 0% 2% 0% 13% 0% 0% 2% 0% 0% 0% 0% 5% 0% 5% 2% 0% 5% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 2% 0% 0% 0% 0% 0% 6% 0% 0%

1% 0% 1% 0% 4% 6% 1% 0% 0% 1% 2% 0% 1% 0% 2% 0% 0% 0% 0% 1% 0% 0% 1% 2% 2% 0% 0% 2% 0% 0% 0% 2% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 1% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0%

0% 2% 21% 11% 9% 0% 0% 1% 5% 5% 0% 0% 0% 0% 1% 23% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 1% 1% 1% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 2% 0% 9% 0% 0% 0% 0% 0% 0% 7% 0% 3% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 9% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 4% 0% 0% 0% 0% 0% 16% 0% 1% 10% 0% 9% 0%

0% 0% 0% 1% 1% 6% 4% 2% 8% 11% 0% 2% 0% 0% 4% 1% 2% 0% 0% 0% 0% 0% 0% 0% 4% 0% 3% 0% 0% 0% 0% 1% 0% 8% 3% 1% 0% 0% 0% 1% 2% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 1% 0% 0% 0%

0% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 77% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 4% 16% 24% 0% 0% 0% 0% 0% 0% 0% 0% 12% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

3% 0% 1% 0% 0% 0% 1% 0% 5% 0% 0% 1% 0% 0% 4% 0% 15% 5% 0% 0% 0% 0% 5% 0% 0% 0% 1% 4% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 1% 0% 0% 0% 0% 0%

TOTAL

0%

3% 1% 3% 7% 6% 1% 2% 0% 0% 0% 1% 1% 8% 0% 0% 10% 1% 0% 0% 0% 1% 0% 0% 6% 2% 0% 0% 0% 0% 0% 19% 0% 2% 3% 0% 0% 1% 0% 0% 0% 1% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0%

Production of tobacco products

0%

0% 0% 1% 5% 0% 3% 0% 0% 2% 1% 0% 5% 0% 0% 0% 0% 1% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0%

Industrial processing of tobacco

0%

11% 22% 0% 0% 0% 0% 24% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 30% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Production of soft drinks and other nonalcoholic beverages

0%

0% 0% 1% 2% 2% 2% 2% 0% 0% 20% 0% 0% 0% 0% 6% 1% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 2% 1% 1% 0% 1% 4% 0% 9% 0% 0% 1% 1% 9% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0%

Production of bottled water

1%

Production of malt, beer and draft beer

0% 0% 0% 8% 0% 0% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 47% 0% 0% 0% 0% 0% 0% 0% 19% 0% 0% 15% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Production of wine

0%

11% 10% 2% 0% 2% 0% 3% 8% 0% 0% 4% 0% 0% 5% 0% 1% 0% 3% 2% 5% 4% 0% 2% 0% 0% 2% 0% 0% 1% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 1% 0% 0% 0% 1% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0%

44% 0%

Production of brandies and other spirits

11%

0% 0% 0% 4% 15% 0% 4% 7% 0% 4% 3% 0% 0% 0% 0% 2% 13% 0% 5% 0% 0% 0% 0% 0% 1% 0% 7% 0% 0% 0% 1% 0% 1% 0% 0% 1% 5% 0% 0% 1% 0% 0% 0% 2% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0%

11% 1%

Production of foodstuffs not previously specified

7%

3% 0% 9% 0% 0% 4% 6% 0% 7% 2% 1% 4% 0% 0% 8% 1% 2% 1% 4% 0% 0% 1% 0% 2% 4% 2% 3% 0% 2% 1% 0% 1% 1% 0% 2% 0% 0% 1% 1% 0% 0% 1% 0% 0% 1% 1% 0% 1% 1% 0% 0% 2% 0% 1% 2% 0%

59% 1%

Production of food and entrees

2%

0% 0% 5% 0% 0% 0% 1% 0% 9% 0% 4% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 4% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 26% 0% 0% 0% 0% 0%

6% 0%

Production of spices, sauces, dressings and condiments

9%

26% 3%

Production of pasta

4% 2%

Production of cocoa by-products, chocolate and fondants

Production of refined sugar

1% 18%

Production of cookies and crackers

Production of raw sugar

3% 1%

Production of bakery products

Milling and production of plant products not previously specified

2% 6%

Production of coffee-based products

Production of animal food

28% 0%

Coffee roasting and grinding

Production of starches and vegetable starches and maize oils

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

12% 2%

13% 2%

3% 4%

20% 4%

32% 1%

6% 0%

2% 0%

9% 2%

2% 4%

0% 0%

10% 0%

14% 7%

0%

0%

0%

1%

0%

4%

2%

0%

4%

0%

20%

5%

0% 0% 4% 5% 0% 7% 2% 0% 2% 35% 0% 1% 0% 0% 4% 0% 0% 1% 0% 0% 0% 0% 2% 1% 0% 1% 0% 0% 1% 0% 0% 11% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

10% 6% 15% 2% 2% 0% 8% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 15% 0% 12% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 2% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 3% 1% 0% 1% 6% 0% 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 36% 0% 0% 30% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3%

2% 0% 7% 1% 3% 3% 2% 0% 5% 0% 1% 3% 1% 0% 11% 0% 5% 0% 1% 0% 0% 0% 1% 0% 1% 1% 1% 0% 0% 0% 4% 0% 1% 0% 0% 0% 1% 1% 1% 0% 0% 0% 0% 6% 0% 1% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0%

2% 0% 14% 1% 0% 2% 1% 0% 12% 8% 0% 0% 0% 0% 0% 5% 0% 10% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 34% 0% 0% 0% 0% 13% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 34% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0%

0% 0% 4% 10% 3% 4% 0% 0% 7% 4% 0% 4% 0% 0% 0% 7% 2% 0% 1% 0% 0% 16% 3% 0% 0% 0% 1% 6% 2% 1% 0% 1% 0% 1% 0% 2% 1% 0% 1% 1% 2% 0% 3% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 3%

0% 0% 7% 9% 11% 2% 0% 2% 2% 2% 0% 0% 0% 0% 4% 4% 7% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% 0% 0% 0% 11% 0% 2% 4% 0% 2% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

1% 1% 0% 9% 9% 15% 1% 2% 5% 1% 1% 2% 1% 0% 0% 3% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 11% 0% 0% 0% 1% 0% 0% 1% 0% 4% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 2%

0% 0% 0% 0% 0% 0% 0% 0% 0% 8% 0% 0% 89% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0%

0% 0% 0% 23% 0% 0% 0% 0% 0% 0% 0% 21% 20% 0% 0% 1% 1% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

4% 3% 3% 3% 3% 3% 3% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

33

Production of corn flour and by-products, except maize oils

Production of manioc flour and by-products

Wheat milling and production of by-products

Rice processing and production of rice products

Production of ice cream and other edible iced products

Dairy production

Preparation of milk

Production of margarine and other vegetable fats and of non-edible animal oils

Production of refined vegetable oils, except maize oil

Production of raw vegetable oils, except maize oil

Production of fruit juices, vegetables and legumes

Production of canned vegetables and other vegetables

Production of canned fruit

Preservation of fish and production of fish products

Production of meat products

Slaughter of pigs, birds and other small animals

MESO-REGION

Slaughter of cattle, except pigs

CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

Northeastern Bahia 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Southeastern Rio Grande do Sul 0% 0% 0% 4% 0% 0% 0% 1% 0% 0% 0% 0% 0% 16% 0% 0% 0% Central Espírito Santo 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% Jaguaribe 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% Pernambuco countryside 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 2% 1% 0% 0% 0% 0% 8% Northern Maranhão 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 1% 0% 0% Western Tocantins 3% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Vale São-Franciscano da Bahia 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% Southern Central Bahia 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% Western Minas Gerais 0% 1% 0% 0% 0% 0% 1% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% Northern Central Mato Grosso do Sul 1% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% Western Rio Grande do Norte 0% 0% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northern Coast of Espírito Santo 0% 0% 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Southeastern Pará 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northern Rio de Janeiro State 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northeastern Pará 0% 0% 0% 2% 0% 0% 0% 7% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northern Central Piauí 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% Southern Bahia 0% 0% 0% 0% 0% 6% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% Eastern Goiás 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Eastern Maranhão 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northwestern Minas Gerais 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 13% 1% 0% 0% 0% 0% 0% Northern Minas Gerais 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% Western Central Paraná 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 14% 0% Eastern Mato Grosso do Sul 1% 0% 0% 0% 0% 2% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% Southern Espírito Santo 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% Central Rio de Janeiro State 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% Alagoas Countryside 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 8% Northern Central Bahia 0% 1% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northwestern Rio de Janeiro State 0% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northern Ceará 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Southwestern Mato Grosso 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northwestern Espírito Santo 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Far West Bahia 1% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% Madeira-Guaporé 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% São Francisco in Pernambuco 0% 0% 0% 0% 5% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Sergipe Countryside 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Great Florianópolis 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% Pernambuco Wilderness 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Western Maranhão 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Mucuri Valley 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northeastern Mato Grosso 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Acre Valley 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Paraíba Countryside 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% Northwestern Goiás 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Eastern Tocantins 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% Southern Maranhão 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northwestern Ceará 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Southern Central Paraná 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Northern Roraima 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% Southern Amapá 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% Northern Piauí 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Southern Ceará 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Rio Grande do Norte Countryside 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Paraíba Wilderness 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Mato Grosso do Sul Wetlands 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Low Amazonas 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Marajó 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Ceará Wilderness 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Jequitinhonha 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% São Paulo South Coast 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% TOTAL 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Source: Softex Monitoring Centre, based on data from RAIS/MTE.

34

TOTAL

Production of tobacco products

Industrial processing of tobacco

Production of soft drinks and other nonalcoholic beverages

Production of bottled water

Production of malt, beer and draft beer

Production of wine

Production of brandies and other spirits

Production of foodstuffs not previously specified

Production of food and entrees

Production of spices, sauces, dressings and condiments

Production of pasta

Production of cocoa by-products, chocolate and fondants

Production of cookies and crackers

Production of bakery products

Production of coffee-based products

Coffee roasting and grinding

Production of refined sugar

Production of raw sugar

Milling and production of plant products not previously specified

Production of animal food

Production of starches and vegetable starches and maize oils

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 7% 0% 0% 2% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 4% 2% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 1% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 6% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 1% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 1% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 1% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

35

CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

TABLE 1.A4 – SEGMENT-WISE AND MESO-REGION-WISE DISTRIBUTION OF PROFTIC IN AGRICULTURE – BRAZIL, 2014

36

Sugarcane crops

Tobacco crops

Soybean crops

Seasonal crops of oleaginous plants, except soybean

Seasonal crops of plants not previously specified

Horticulture

Cultivation of flowers and ornamental plants

Orange crops

Grape crops

Cultivation of fruits of permanent crops, except oranges and grapes

Coffee crops

Cocoa crops

Cultivation of plants of permanent crops not previously specified

Production of certified seeds

Production of seedlings and other certified forms of plant propagation

São José do Rio Preto 0% Assis 0% Ribeirão Preto 3% Brazilian Federal District 1% Southern Goiás 3% Minas Gerais’ Triangle/High Paranaíba 4% Campinas 0% Northern Mato Grosso 1% Southeastern Mato Grosso 3% Belo Horizonte Metro Area 10% São Paulo Metro Area 4% Eastern Mato Grosso do Sul 0% Bauru 0% Southwestern Mato Grosso do Sul 0% Downtown Goiás 0% Northeastern Pará 1% Southwestern Mato Grosso 4% Pioneer Northern Paraná 0% Northern Central Mato Grosso do Sul 0% Far West Bahia 3% Eastern Central Rio Grande do Sul 0% Northern Minas Gerais 0% Western Paraná 0% Fortaleza Metro Area 1% Southern/Southwestern Minas 0% Itapetininga 0% Araçatuba 0% Araraquara 0% Macro São Paulo Metro Area 0% Southern Central Mato Grosso 0% Western Minas Gerais 2% Eastern Sergipe 1% Northern Central Paraná 1% Espírito Santo North Coast 0% Porto Alegre Metro Area 0% Belém Metro Area 0% Rio de Janeiro Metro Area 7% Piracicaba 0% Southeastern Pará 0% Western Maranhão 3% Southern Maranhão 0% Jaguaribe 0% Northwestern Minas Gerais 4% Pernambuco Countryside 0% Recife Metro Area 3% Southern Bahia 1% Vale do Rio Doce 1% Downtown Amazonas 0% Southern Central Paraná 1% Western Santa Catarina 0% Northeastern Bahia 3% Downtown Minas Gerais 0%

Crops of upland cotton and other seasonal crop fibers

MESO-REGION

Cereal crops

PROFTIC Total: 2,568

0% 0% 0% 0% 0% 3% 0% 6% 46% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 37% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0%

18% 17% 11% 0% 9% 0% 7% 0% 0% 0% 0% 2% 4% 4% 3% 0% 3% 3% 0% 0% 0% 1% 0% 0% 0% 1% 2% 1% 0% 0% 1% 2% 1% 1% 0% 0% 0% 0% 0% 0% 1% 0% 0% 1% 0% 0% 0% 1% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 2% 0% 10% 0% 0% 24% 20% 0% 1% 3% 0% 2% 0% 0% 1% 1% 12% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 2% 0% 0% 0% 0% 0% 1% 0% 0% 0%

0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 3% 0% 5% 10% 0% 0% 3% 0% 5% 0% 0% 0% 0% 0% 0% 0% 3% 10% 0% 0% 5% 0% 0% 3% 0% 0% 8% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 30% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 8% 0% 8% 4% 0% 0% 0% 8% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 40% 0% 0% 0% 0% 0% 0% 0% 12% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0%

9% 0% 0% 0% 0% 0% 55% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 9% 0% 0% 0% 9% 0% 0% 9% 0% 0% 9% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 19% 23% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 42% 0% 0% 0% 0% 0% 0% 0% 0% 0% 12% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 10% 0% 0% 0% 4% 0% 14% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 2% 0% 16% 0% 0% 0% 0% 0% 6% 0% 0% 0% 2% 0% 0% 0% 18% 0% 0%

0% 0% 3% 0% 0% 32% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 13% 0% 0% 0% 0% 16% 0% 0% 0% 0% 0% 16% 0% 0% 3% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0%

0% 0% 2% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 4% 70% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 11% 0% 0% 0% 2% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0%

0% 0% 3% 0% 5% 16% 0% 0% 0% 3% 3% 0% 0% 0% 2% 0% 0% 0% 0% 0% 48% 2% 0% 0% 0% 0% 2% 2% 0% 0% 0% 0% 0% 0% 3% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 67% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Cattle farming

Farming of other large-sized animals

Swine breeding

Poultry breeding

Agricultural-supporting activities

Livestock-supporting activities

Post-harvesting activities

Forest production - planted forests

Forest production - natural forests

Forest production-supporting activities

Saltwater fishing

Aquaculture in saltwater and brackish water

Aquaculture in fresh water

TOTAL

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

13% 0% 4% 0% 3% 5% 2% 4% 1% 6% 10% 3% 2% 1% 4% 0% 3% 0% 1% 2% 0% 0% 0% 0% 3% 0% 2% 0% 0% 3% 0% 0% 0% 0% 0% 0% 2% 1% 7% 0% 0% 0% 3% 0% 0% 0% 1% 0% 0% 0% 0% 1%

0% 0% 67% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 17% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 17% 0% 6% 0% 17% 0% 0% 0% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 11% 0% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 17% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

3% 0% 0% 7% 0% 13% 0% 2% 1% 2% 0% 0% 0% 1% 1% 0% 0% 0% 0% 0% 0% 0% 8% 8% 3% 3% 0% 1% 2% 0% 1% 0% 0% 0% 4% 0% 0% 2% 0% 0% 0% 0% 0% 2% 2% 0% 0% 0% 0% 0% 0% 0%

2% 0% 2% 48% 2% 4% 3% 0% 1% 4% 6% 1% 0% 1% 0% 0% 0% 0% 0% 1% 0% 1% 2% 1% 1% 0% 1% 0% 1% 3% 0% 0% 0% 0% 1% 1% 0% 1% 1% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 4% 4% 46% 4% 0% 0% 7% 4% 0% 0% 0% 0% 0% 0% 4% 7% 0% 0% 4% 0% 0% 7% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 15% 15% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 15% 0% 0% 5% 0% 0% 0% 0% 25% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 10% 0% 0%

0% 1% 1% 0% 1% 1% 3% 0% 0% 0% 4% 19% 0% 0% 0% 0% 0% 0% 0% 0% 0% 8% 0% 0% 3% 4% 3% 0% 0% 1% 0% 0% 0% 0% 1% 0% 0% 0% 3% 5% 0% 0% 0% 0% 0% 0% 0% 0% 13% 4% 3% 4%

0% 0% 0% 0% 0% 0% 0% 0% 0% 58% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 8% 0% 0% 0% 0% 0% 8% 0% 0% 8% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 1% 0% 0% 0% 0% 0% 0% 13% 7% 1% 0% 0% 0% 0% 0% 1% 0% 0% 0% 2% 0% 0% 0% 5% 0% 0% 1% 2% 0% 0% 0% 5% 4% 1% 2% 2% 2% 6% 1% 0% 2% 0% 0% 2% 12% 0% 0% 0% 6% 6%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 29% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 14% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 23% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 15% 0% 0% 8% 8% 0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0% 0% 25% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

8% 7% 6% 5% 5% 4% 3% 3% 3% 2% 2% 2% 2% 2% 2% 2% 2% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1%

37

Presidente Prudente Great Florianópolis Southwestern Rio Grande do Sul Campo das Vertentes Southwestern Paraná Northeastern Rio Grande do Sul Northeastern Mato Grosso São Francisco in Pernambuco Eastern Goiás Zona da Mata Eastern Central Paraná Northwestern Rio Grande do Sul Northern Ceará Pernambuco Woods Salvador Metro Area Northern Rio de Janeiro State Southwestern Piauí Southern Espírito Santo Southeastern Rio Grande do Sul Western Tocantins Vale São-Franciscano in Bahia Southern Central Bahia Jequitinhonha Western Central Paraná Curitiba Metro Area Eastern Tocantins Eastern Maranhão Eastern Rio Grande do Norte Serrana Vale do Itajaí Paraíba Countryside Paraíba Woods Eastern Alagoas Central Espírito Santo Marília Northern Santa Catarina Western Central Rio Grande do Sul Eastern Rondônia Low Amazonas Northern Maranhão Central Maranhão Northern Piauí Southeastern Paraná Mato Grosso do Sul Wetlands Acre Valley Pernambuco Wilderness Sergipe Countryside Northwestern Goiás Madeira-Guaporé Northwestern Ceará Western Rio Grande do Norte Northern Central Bahia Mucuri Valley Northern Goiás Total

Production of seedlings and other certified forms of plant propagation

Production of certified seeds

Cultivation of plants of permanent crops not previously specified

Cocoa crops

Coffee crops

Cultivation of fruits of permanent crops, except oranges and grapes

Grape crops

1% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 10% 0% 0% 0% 0% 33% 0% 3% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 75% 2% 0% 0% 0% 0% 0% 2% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 25% 4% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 8% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 5% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Source: Softex Monitoring Centre, based on data from RAIS/MTE. 38

Orange crops

Cultivation of flowers and ornamental plants

Horticulture

Seasonal crops of plants not previously specified

Seasonal crops of oleaginous plants, except soybean

Soybean crops

Tobacco crops

Sugarcane crops

Crops of upland cotton and other seasonal crop fibers

MESO-REGION

Cereal crops

CHAPTER 1 - TECHNOLOGY AND MARKET DYNAMICS FOR THE SOFTWARE & ICT SERVICES CHAIN FOCUSED ON AGRIBUSINESS

TOTAL

Aquaculture in fresh water

Aquaculture in saltwater and brackish water

Saltwater fishing

Forest production-supporting activities

Forest production - natural forests

Forest production - planted forests

Post-harvesting activities

Livestock-supporting activities

Agricultural-supporting activities

Poultry breeding

Swine breeding

Farming of other large-sized animals

Cattle farming

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 5% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 6% 0% 0% 0% 1% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 8% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 1% 0% 4% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 17% 17% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 50% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 0% 0% 0% 0% 0% 0% 0% 1% 14% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 14% 0% 0% 0% 1% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 6% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 31% 0% 0% 0% 0% 0% 0% 0% 0% 15% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 14% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 14% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 25% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 1% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 15% 0% 0% 0% 0% 0% 0% 0% 0% 0% 4% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 8% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

39

CHAPTER 2 - ICT IN AGRIBUSINESS: TECHNOLOGICAL TRENDS AND BUSINESS OPPORTUNITIES

40

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

CHAPTER

2

ICT IN AGRIBUSINESS: TECHNOLOGICAL TRENDS AND BUSINESS OPPORTUNITIES INTRODUCTION HISTORY The more intensive use of Information and Communication Technologies, hereinafter referred to only as ICT, in agribusiness started in the 1980s, as of the introduction of microcomputers in the market, mainly because they offered a reasonable computational processing capacity at an affordable price, considering the technological stage at that time. Sonka (1985), in the then newly released publication “Computers and Electronics in Agriculture” already pointed out the great opportunity of using computers to manage information in American farms, with the following words: “The last decade has witnessed dramatic changes in the cost and availability of computing capacity in agriculture. Formerly computers were thought of as tools appropriate only for university researchers and managers of large agribusiness firms. Now relatively inexpensive business computers are available for use by farmers and their advisors. This dramatic decline in the real cost of computing capability for use on commercial farms has led to predictions of major changes in the agricultural production firm”. Usage of ICT in agribusiness was not what was dreamed. However, after little more than thirty y ears, we must recognize that the use of ICT in the agribusiness was not what was dreamed, at least compared to the impact of the adoption of ICT in other sectors of the economy. Maybe this will happen in the coming years, due the learning curve, the farmer culture, the oldness of new entrepreneurs in the wings of their startups and to the technological trends and new business opportunities. Basically, the history of use of ICT in agribusiness may be split into before and after the Internet.

41

CHAPTER 2 - ICT IN AGRIBUSINESS: TECHNOLOGICAL TRENDS AND BUSINESS OPPORTUNITIES

Before the Internet Before the Internet: application of ICT was basically limited to the use of microcomputers by farmers themselves and by consultants to perform laborious and tedious calculations. In the period prior to the commercial launch of the Internet in the 90s, application of ICT in agribusiness was basically limited to the use of microcomputers by farmers themselves and by consultants working for them, i.e., agronomists, veterinarians, suppliers, accountants, etc. It was quite common to use VisiCalc® spreadsheets and accounting programs, programs that calculated the minimum cost of feed, fertilization and liming and irrigation, among others. In short, it could be said that microcomputers were basically used to perform laborious and tedious calculations. Some applications used data communication through ordinary telephone networks connected to modem cards in the microcomputers through the so-called BBS1 to broadcast information about the weather, the market and even for data communications of personal use, in a precursor system of the Internet.

After the Internet After the Internet: the way people used computers (and more recently, phones) changed, allowing the electronic exchange of data and information on a global scale at low cost. The Internet has forever changed the way computers are used, especially microcomputers, and more recently, phones. It allowed the electronic exchange of data and information on a global scale at a relatively very low cost compared to the traditional phone systems, and with an unprecedented level of agility and flexibility in human history. The Internet began as a military project, Arpanet2. In the 70s, it was expanded to the major universities of the world, and turned into a commercial product in the mid-90s. By the year 2000, it was believed that the “Internet business” was so promising that several companies launched very ambitious projects, mainly of e-commerce. And agribusiness, because it is such a thriving economic sector, did not escape the radar of companies. At that time, countless projects of Internet use to facilitate the marketing of agricultural inputs and products online were launched at a global scale, and also in Brazil.

DREAMS AND REALITIES The everyday automation is the most common vision when it comes to thinking about the future. Not only science fiction writers have long foresaw a future filled with machines and robots; even the Brazilian writer Guimarães Rosa, in his masterpiece “Grande Sertão: Veredas”, suggested this futuristic vision. This appears clearly in the thoughts of the wise roughneck Riobaldo (Almeida, 1999): “For the ancients themselves did not know that a day will come when we can remain lying on a hammock or a bed, and our garden hoes will move by themselves to weed the fields, and the sickles will harvest by themselves, and the car going by i tself to pick up the harvest and everything, which is not the man, it’s his, his, obedience?” (G. Rosa, 1956)

1 BBS - Bulletin Board System - bit.ly/AgroTic_BBS. 2 Arpanet - Advanced Research Projects Agency Network - bit.ly/AgroTic_Arpanet

42

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

Riobaldo’s dream shows the desire of the urban man to live in the countryside and use the city technologies.

The visionary dream of the great writer is now technically and fully possible with the precision agriculture. Basically, Riobaldo’s desire shows, unconsciously perhaps, the desire of the urban man to live in the countryside and use city technologies. Much of the ICTs produced for farmers are thought of from the top down, i.e., without market research to know what really matters and can really help the farmer in his daily toil.

The Green Revolution, which had its heyday in the 70s and 80s, avoided the catastrophic scenario of food shortage that people kept believing in, allowing the spectacular increase in food production. Agriculture and livestock have historically been laggards in the use of modern technologies: mechanization, chemistry, genetics and IT. In the mid-50s, there used to be a customary prediction that the world would go through a catastrophic scenario of food shortage due to the projections of population growth and the capacity of increasing food production. This did not materialize due to the so-called Green Revolution, i.e., the adoption of these modern technologies mentioned, especially the use of improved or genetically modified seeds, but also of mechanization, fertilization for soil correction, use of crop protection, etc. That secured a dramatic increase in food production, either by productivity gains (measured in tons of food produced per hectare of cultivated land) or by the expansion of arable land (in Brazil, for example, the use of Cerrado vegetation, which used to be considered a non-productive area). Rocked by the dreams come true of the Green Revolution, which peaked in the 70s and 80s, many belivied that the use of computers in agriculture would impact as much or even more than the technologies that were underlying during the Green Revolution. Thus, many dreams rocked with bits and bytes were built from the 80s and peaked in the 90s. A new Revolution, this time computerized and disseminated among farmers, is yet to happen. There isn’t yet a company leader in the ICT market for agribusiness. In fact, the new computerized Green Revolution is yet to happen, for it is still a dream in relation to its massive dissemination between farmers, as was the case with the mechanization of farming, the use of chemicals in soil correction and in the elimination of pests and weeds, and the use of genetically improved or modified seeds. Perhaps, due to this reason, there is not yet a company leader in the ICT market for agribusiness, neither in Brazil nor in any other country in the world, as there are in other technologies behind the Green Revolution, for example: John Deere3 in agricultural mechanization; Monsanto4 in the segment of genetically modified seeds; Bayer5 in the agrochemical area; etc.

3 Jonh Deere: www.deere.com 4 Monsanto: www.monsanto.com 5 Bayer: www.bayer.com

43

CHAPTER 2 - ICT IN AGRIBUSINESS: TECHNOLOGICAL TRENDS AND BUSINESS OPPORTUNITIES

THE FUTURE ICT initiatives for agribusiness are still very focused on universities . There is plenty to be done to stimulate the use of ICT in agribusiness. Much of what has been done happens in universities and agricultural research institutions worldwide. In Brazil, for example, some public universities stand out - such as Federal University of Viçosa (UFV); Federal University of Lavras (UFLA); Escola Superior de Agricultura Luiz de Queiroz (Esalq) from USP, in Piracicaba; Federal Rural University of Rio de Janeiro (UFRRJ); Federal Rural University of Pernambuco (UFRPE); etc. - and Embrapa (Brazilian Agricultural Research Corporation). The overwhelming majority of companies that develop ICT solutions for agribusiness has the profile of small business, operating in very specific market niches, what will be seen later in this chapter. The pursuit of improvement in the agribusiness management with intensive ICT usage is still underexplored. Most probably, the offer does not meet the needs of customers, which increases the cost of adapting the business to the technology. The pursuit of improvement in the agribusiness management with intensive ICT usage, either in farms or in the large companies of the agricultural industry chains, is a market still underexplored. Not because the offer of products is lacking, but most likely because what is offered, before it can solve the customer’s problems, immediately increases the cost of adapting its business to the new technology. Market research is absolutely necessary to reverse this situation. Through them, it would be possible to determine the farmers’ actual problems and use this generated knowledge to develop solutions that, in fact, meet their needs. This makes farmers realize that the investment in new technologies brings tangible benefits, i.e., an advantageous cost-effectiveness.

2.1 ICT-SUPPORTING ORGANIZATIONS IN AGRIBUSINESS In the 70s, the first networks dedicated to computationally solve problems of the agricultural sector come to fruition. The emergence of a community mostly made up of academics, organized and dedicated to computationally solve problems in the agricultural sector date from the early 70s. October 1985 sees the release of the first issue of publication “Computers and Electronics in Agriculture6” which continues being published today (Figure 2.1). The opening editorial (Lambert, 1985) of this publication already glimpsed what today are called the ICTs applied to agribusiness, calling them “silicon technology, in its broadest sense, to agriculture” as may be seen in this passage:

6 Computers and Electronics in Agriculture: bit.ly/AgroTic_CompElectAgriculture.

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

“This journal is meant to be an international meeting place for readers and writers to share ideas, experiences and data about the application of silicon technology, in its broadest sense, to agriculture. Other well developed journals already serve those scientists, engineers and economists interested in more basic activities, e.g. chip technology, computer architecture, language development and accounting methods. The emphasis of this journal is on application of the basics. We hope authors will describe state-of-the-art applications and therefore challenge thinkers and doers.” FIGURE 2.1 – COVER OF A RECENTLY PUBLISHED ISSUE OF PUBLICATION “COMPUTERS AND ELECTRONICS IN AGRICULTURE”

This section lists the key organizations dedicated to bringing together professionals focused on the development of studies, researches and ICT applications in agribusiness around the world.

BRASIL

Agrosoft Agrosoft e merged in 1993 as one of the 13 centers of the CNPq (the Brazilian National Council for Scientific and Technological Development) program called Softex, currently coordinated by an independent civil society, the Association for the Promotion of Brazilian Software Excellence, Softex. Headquartered in Juiz de Fora (MG), at the Federal University of Juiz de Fora (UFJF) also had branches in Viçosa (MG), at the Federal University of Viçosa (UFV), and in Lavras (MG), at the Federal University of Lavras (UFLA), two institutions with a strong tradition and recognition in the development of agricultural researches and in the education of graduates, masters and doctors in this sector. Agrosoft organized six Agrosoft7 Conventions: in 1995 in Juiz de Fora (MG), in 1997 in Belo Horizonte (MG), in 1999 in Campinas (SP), in 2000 in Uberlândia (MG), in 2002 in Brasília (DF), in 2004 in Lisbon (Portugal), and in 2005 in Lima (Peru), in addition to promoting the Brazilian participation in the Global Soy Forum, in Chicago (USA), in 1999. Eleven issues of Revista Agrosoft (Agrosoft Magazine) were also published; and three issues (1995, 1997 and 1999) of the first Brazilian Guide of Agricultural Software were published. The institution also 7 Agrosoft Conventions: bit.ly/AgroTic_AgrosoftCongressos (In Portuguese).

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served in implementing incentives sponsored by CNPq and by Fapemig (the Minas Gerais State ResearchSupporting Foundation) of ICT business plans for agribusiness. In Agrosoft’s events and publications, there has always been a significant participation, both of professionals from the academic world and from the business sector. Agrosoft was the first organized body of professionals, institutions and companies dedicated to ICT development for agribusiness in Brazil.

SBIAgro: The Brazilian Association of Information Technology in Agriculture In 1995, during Agrosoft 95 in Juiz de Fora (MG), the creation of The Brazilian Association of Information in Agriculture - SBIAgro8 was proposed, founded in 1996 (SBIAgro, 1996). The entity publishes the RBIAgro Magazine and has published nine issues, with the first being in 1998, two issues in 1999, one issue in 2000, two issues in 2002, two issues in 2003, two in 2004 and 2005, one issue in 2006, and the last issue in 2009. Periodically, SBIAgro holds the Brazilian Agroinformatics Convention, which first edition was held in 1997, together with Agrosoft 97 in Belo Horizonte. The other editions were held in 1999, also together with Agrosoft 99, in Campinas (SP), in 2002 in Foz do Iguaçu (PR), in 2003 in Porto Seguro (BA), in 2005 in Londrina (PR), in 2007 in São Pedro (SP), in 2009 in Viçosa (MG), in 2011 in Bento Gonçalves (RS), in 2013 in Cuiabá (MT) and the last edition was in 2015, in Ponta Grossa (PR).

EUROPE EUNITA: European Network for Information Technology in Agriculture Project EUNITA (European Network for Information Technology in Agriculture) was a coordinated action among participants from thirteen European countries. The project began on December 1st, 1994, and ended on November 30th, 1997. The purpose was to promote a better quality of agricultural research and management in Europe, through the use of information technologies between researchers, developers and users in the countries of the European Union. The Final Report of project EUNITA (Eunita, 1997) made ten recommendations within the following themes discussed in work groups: 1. Electronic communications and database 2. Register (catalog) of agricultural software 3. Description of intensively-knowledgeable software 4. Impacts of standardization on the development and use of agricultural software 5. Dissemination of information technologies in agriculture

8 SBIAgro: The Brazilian Association for Information Technology in Agriculture - www.sbiagro.org.br (In Portuguese).

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6. Application of information technologies in Southern Europe 7. Application on IT transfer in agriculture in Eastern and Central Europe 8. Modeling, at farm level, of cattle grazing systems 9. Support to decisions in risk conditions 10. European association of information technologies in agriculture

EFITA: European Federation for Information Technology in Agriculture From discussions of the last theme of project EUNITA, the European Federation for Information Technology in Agriculture9 (EFITA) was born, a federation of national European entities whose main purpose is the development of information technologies in agriculture. EFITA comprises the following European national associations: •

Germany: GIL10 - German Association for Informatics in Agriculture, Forestry and Nutrition



Denmark: DSIJ11 - Danish Society for Informatics in Agriculture



Spain: SETIAM - Spanish Information Technology in Agriculture Association



France: AFIA - French Society for Informatics in Agriculture



Georgia: GAIAFE - Georgian Association for Informatics in Agriculture, Food and the Environment



Great Britain: BAITA - British Association for Information Technology in Agriculture



Greece: HAICTA - Association for Information and Communications Technology in Agriculture



The Netherlands: VIAS - Dutch Society for Informatics in Agriculture



Hungary: HAAI12 - Hungarian Association of Agroinformatics



Ireland: ISITA13 - The Irish Society for Information Technology in Agriculture



Italy: AITICA - Italian Society for Information and Communications Technology in Agriculture



Poland: POLSITA14 - Polish Society for Information Technology in Agriculture



Portugal: APDTICA - Portuguese Association for Development of Information and Communication Technologies in Agriculture



Czech Republic: CSITA - Czech Society for Information Technology in Agriculture



Sweden: LANTNET - Swedish Association for Information Technology in Agriculture

9 EFITA: European Federation for Information Technology in Agriculture - www.efita.net 10 GIL: German Association for Informatics in Agriculture, Forestry and Nutrition - www.gil-net.de 11 DSIJ: Danish Society for Informatics in Agriculture - www.vias.nl 12 HAAI: Hungarian Association of Agroinformatics - miau.gau.hu/magisz. 13 ISITA: The Irish Society for Information Technology in Agriculture - www.iol.ie/~harkin/isita.htm. 14 POLSITA: Polish Society for Information Technology in Agriculture - www.au.poznan.pl/polsita.

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The first EFITA Convention was held in 1997, in Copenhagen (Denmark) and has been held regularly every two years15. In 1999, it was held in Bonn (Germany); in 2001, in Montpellier (France); in 2003 in Debrecen (Hungary); in 2005 in Vila Real (Portugal); in 2007 in Glasgow (UK); in 2009 in Wageningen (The Netherlands); and in 2011 in Prague (Czech Republic). The last EFITA16 Convention was held in 2015 in Poznan (Poland), and was a joint organization between EFITA and the WCCA (World Convention on Computers in Agriculture) and the CIGR (International Commission of Agricultural and Biosystems Engineering). Seen as it is one of the most important events on ICT in agribusiness, to read the program17 and the abstracts18 of the presentations of the Convention may provide an overview of the trends and the state-of-the-art researches in this area.

OTHER INITIATIVES AND ORGANIZATIONS WCCA: World Congress on Computers in Agriculture The WCCA - World Convention on Computers in Agriculture is an American initiative headed by Professor Fedro S. Zazueta19 from the University of Florida. The WCCA has been associating with federations EFITA and AFITA, among others, as a collaborator in the organization of international conventions. Professor Fedro S. Zazueta is one of the forerunners in the conversations, in the US and around the world, with entities, organizations, associations, universities, researchers and entrepreneurs with an interest in the development of ICT in agriculture and agribusiness in general. AFITA: Asian Federation for Information Technology in Agriculture A AFITA - Asian Federation for Information Technology in Agriculture20 foi fundada em 1998 no Japão e dela são membros as seguintes associações nacionais congêneres: •

JSAI21: Japanese Society of Agricultural Informatics



KSAIS: Korean Society for Agricultural Information



CSAI: Chinese Society of Agricultural Information



TAIN: Thai Agricultural Information Network



ISAIT: Indian Society of Agricultural Information Technology



ISAI: Indonesian Society for Agricultural Information

From June 21st to 24th, 2016, AFITA, partnering with WCCA, held the AFITA 2016 international convention: ICT for Future Agriculture22, in South Korea.

15 Records from EFITA Conventions from 1997 to 2013 - bit.ly/AgroTic_EfitaConventions. 16 EFITA 2015 Convention - www.efita2015.org. 17 EFITA 2015 Convention Programme - www.efita2015.org/programme. 18 EFITA 2015 Convention Abstracts - bit.ly/AgroTic_Efita2015Abstracts. 19 Prof. Fedro S. Zazueta - fsz.ifas.ufl.edu. 20 AFITA: Asian Federation for Information Technology in Agriculture - www.afita.org. 21 JSAI: Japanese Society of Agricultural Informatics - www.jsai.or.jp 22 AFITA 2016 Convention - afita2016.org

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ICT-AGRI: ICT and Robotics for Sustainable Agriculture ICT-AGRI - ICT and Robotics for Sustainable Agriculture23 is a project funded by the European Commission which started in 2009. Its purpose is to strengthen European research in precision agriculture, to develop an European research agenda in ICT and robotics in agriculture. ICT-AGRI develops international calls for research to gather human and financial resources beyond the borders of participating countries to improve the efficiency and effectiveness of the European research efforts in this area. INFITA: International Network for Information Technology in Agriculture INFITA - International Network for Information Technology in Agriculture24 was founded in 2003 during the fourth EFITA Convention in Hungary, aiming to provide the basis for cooperation between national and international organizations and associations all over the world with an interest in research and use of ICT in agriculture, food and environment. These are INFITA’s international organizations and national federations and associations, including SBIAgro: International Organizations: •

CIGR - International Commission of Agricultural Engineering25



FAO - Food and Agriculture Organization26



IAALD - International Association of Agricultural Information Specialists27



IFAC - International Federation of Automatic Control28

Federations: •

AFITA - Asian Federation for Information Technology in Agriculture



EFITA - European Federation for Information Technology in Agriculture, Food and the Environment



PanFITA - Pan-American Federation for Information Technology in Agriculture

Agriculture National Associations: •

AFIA - French Society for Informatics in Agriculture



AITICA - Italian Society for Information and Communications Technology in Agriculture



APDTICA - Associação Portuguesa para o Desenvolvimento das Tecnologias de Informação na Agricultura



ASABE - American Society of Agricultural and Biological Engineers



ASITA - Australian Society for Information Technology in Agriculture

23 24 25 26 27 28

ICT-AGRI: ICT and Robotics for Sustainable Agriculture - www.ict-agri.eu INFITA - www.infita.org CIGR - www.cigr.org FAO - www.fao.org IAALD - www.iaald.org IFAC - www.ifac-control.org

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BAITA - British Association for Information Technology in Agriculture



DaNET - Danish Agricultural Network in Engineering and Technology



DINA - Danish Informatics Network in the Agricultural Sciences



DSIJ - Danish Society for Informatics in Agriculture



GAIAFE - Georgian Association for Informatics in Agriculture, Food and the Environment



GIL - German Society for Informatics in Agriculture, Forestry and Food



HAAI - Hungarian Association of Agroinformatics



HAICTA - Hellenic Association for Information and Communications Technology in Agriculture



IAES - Iranian Agricultural Economics Society



IIAA - International Institute of Agroinformatics and Agromanagement (Índia)



INSAIT - Indian Society of Agricultural Information Technology



ISAI - Indonesian Society of Agricultural Informatics



ISITA - Irish Society for Information Technology in Agriculture



JPPN - National Agricultural Research Network (Indonésia)



JSAI - Japanese Society of Agricultural Informatics



KSAIS - Korean Society of Agricultural Information Science



POLSITA - Polish Society for Informatics in Agriculture, Forestry and Food Industry



SAITA - Swedish Association for Information Technology in Agriculture



SBIAgro - The Brazilian Association of Information Technology in Agricuture



SETIAM - Spanish Information Technology in Agriculture Association



VIAS - Dutch Society for Informatics in Agriculture

2.2 TRENDS AND OPPORTUNITIES Technological trends and business opportunities in ICT for the agricultural sector: traceability and food safety; decision support systems; mobile applications; web service and applications, data exchange and information; and accelerators to boost startups. This section highlights some technological trends and business opportunities in ICT in the agricultural sector. These conclusions are partly based on works29 presented in the latest EFITA - European Federation of Information Technology in Agriculture Convention, held in 2015, in Poland, and supported by additional surveys.

29 Efita 2015 Convention Abstracts - bit.ly/AgroTic_Efita2015Abstracts.

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The EFITA Convention was organized in six sessions listed below, anticipating the relative importance of these issues today: 1. Traceability and food safety. 2. Decision support systems. 3. Mobile applications (apps). 4. Web services and applications for farms. 5. Exchange of data and information, knowledge repositories. 6. Company accelerators: the Internet of the Future in the agricultural sector.

MOBILE APPLICATIONS (APPS) Mobile applications are certainly a relevant market niche, which grows significantly all over the world. But there are doubts about its chance of success, quite supported in the interest of farmers to use technology. The expansion of mobile Internet broadband networks and the popularity of smartphones, given their versatility, ease of use and processing capacity, and also their relatively affordable cost, have encouraged small entrepreneurs to develop hundreds of applications (apps) for these devices to be used in farms and throughout the agribusiness chain. This has made it possible to increase the employment capillarity of ICT in agribusiness. This is a worldwide phenomenon, as is evident from the emphasis given to agri food apps at EFITA 2015 Convention, which dedicated an entire session for papers on the subject. Despite the attention given and the enthusiasm perceived around this trend, it is also worth noting a certain distrust that lingers in the air. This is because we may be seeing a rerun of a phenomenon occurred in the last century, in the 80s and 90s, when microcomputers became available. At the time, it was thought that there would be a strong adoption of ICT by farmers and ranchers, which indeed did not happen. The apps phenomenon also occurs in Brazil, as can be seen in several articles published by the press (see Appendix 2.A1), citing applications focused on agribusiness, on the sustainable use of the environment and food in general. This is, therefore, a promising market niche.

PRECISION AGRICULTURE Precision agriculture is basically gathering geo-referenced information on the conditions of soil, crop, harvest, climate, etc., in order to precisely assist farmers in their decision making process, in order to increase productivity and profitability in their business. For example, instead of uniformly applying an agricultural nutrient on the land to be planted, the instruments of precision agriculture let you know what is the exact need of that nutrient in each square meter of the land, thus allowing a much more rational application from the economic and environmental points of view, without wasting.

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Precision agriculture was used in the 90s in the US. The lack of software support and expert advice, along with the cultural resistance of farmers are among the reasons for failure in the adoption of that technology back then. Figure 2.2, drawn up in 1996, shows a schematic model of precision agriculture the way it was imagined back then. Sensors installed on satellites, aircrafts, drones (UAVs - unmanned aerial vehicles) or on stationary or moving equipment on the soil capture images and signals and transmit them to computers that process the collected data, extracting information, which in turn feed the control mechanisms of seeders, combine harvesters, etc. In this model, the central character was the farmer himself, whom, in theory, should be able to absorb the technology, applying it by himself. FIGURE 2.2 – PRECISION AGRICULTURE: SCHEMATIC VIEW, INCLUDING DATA COLLECTION AND OPERATION AND INTERACTION OF INSTRUMENTS ADOPTED

Source: Sonka & Coaldrake (1996).

This precision agriculture model had its heyday in the late 90s in the US. Several American farmers used technology, gathering thousands of data on their crops through satellite photos and collection devices installed in stationary or moving equipment, but were unable, however, to extract useful information, either due to lack of software support and expert advice for data analysis, or to the cultural resistance of the farmer himself. Thus, although precision agriculture has been identified at the time as a hope for defining new paths in agricultural management, the lack of field-tested experiments did not allow a firm recommendation to adopt the technology on a larger scale, as mentioned in a study of the National Research Council (1997).

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Now, we experience a different moment. Hopes are renewed with the use of drones for data collection and supply of specialized processing and analysis services. Nowadays, hopes are renewed due to the entry of drones on the scene. These small aircrafts or unmanned helicopters enable the installation of cameras and sensors that capture signals from the land, plantations or flocks, and are able to carry small sprays of agricultural inputs, i.e., fertilizers, pesticides, etc. and apply them precisely where they are needed. The video30 made by Embrapa Instrumentação Agropecuária located in São Carlos (SP) shows, in a clear and didactic way, the perspectives of drone usages in precision agriculture. In the video, Lúcio André de Castro Jorge, an engineer at Embrapa, and Bruno Squizato Faiçal, doctoral student at the Institute of Mathematics and Computer Sciences from the State University of São Paulo (USP), talk about the possibilities of using drones to assist farmers in increasing productivity and reducing the economic and environmental costs of agricultural activities. The use of drones in precision agriculture has been stimulating the emergence of specialized companies in the remote piloting of unmanned aircrafts, in gathering and analyzing the acquired data and in supporting farmers in their decision making process. The arrival on the scene of experts supported by technology allows not only the offer of a customized service tailored to farmers, but mainly the contracting of a service based on a well thought out cost-effectiveness analysis. In terms of business model, it is a completely different situation from that observed in the 90s, when farmers were encouraged to buy seeders and combine harvesters with a precision agriculture system already embedded, more as a marketing appeal than for any effective advantage by using them. As the farmer was not properly trained to analyze the data collected, the perception was that the cost of purchasing the necessary hardware was relatively low (compared to the cost of a seeder or a combine harvester), but the benefit was zero or practically zero. At that time, it was common to find farmers in the US with thousands of data collected about their farming activities without using them. In short, in the 90s, there was an attempt to ‘push’ precision agriculture down through farmers’ throats as a ‘modern-day knickknack’ for the quite expensive seeders and combine harvesters. Now there is the offer of a precision agriculture service using drones, in which the contracting of the service is based on a costeffectiveness analysis. The chance for a new business model to work, now, appears to be so much higher.

TRACEABILITY AND FOOD SAFETY It is in Europe and Japan that the traceability and food safety theme has been treated with the utmost seriousness. In Brazil, the subject is strengthened in the context of exports. Concerns on food safety, understood as the right to consume food without risking your health, entered the radar of governments and of the more enlightened members of society after the phenomenon of the ‘mad cow’ disease. 30 Video «Drones over the Field” at www.agrosoft.org.br/?v=cYqroD2fqkw (In Portuguese).

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“The mad cow disease, Bovine Spongiform Encephalopathy (BSE), reached over 180,000 cases in England since it was identified in 1986. To date there are only theories about the possible cause of the disease. The most acceptable of them would be the incorporation of remains of sheep that are sick with scrapie (a neurodegenerative disease such as BSE) in the feed used to feed the English cattle. Due to its inconsistencies, a group of researchers suggests that the primary cause is linked to the incorporation of remains of dead humans in the diet of the cattle that was imported from India along with animal carcasses for the production of fertilizers and animal feed in the Old World.”31 From there, especially in the European Union, several initiatives were created to ensure food safety, “from farm, to fork”, preventing consumers from being exposed to health risks when consuming foods. For this, several food traceability projects using the support of ICT were funded by the European Commission. Despite the fact that food safety should be treated with the utmost seriousness in any country, it is mainly in Europe and Japan that the issue has been seriously considered. In Brazil, the effort to provide food produced domestically with the necessary safety to protect the health of the population is basically considered within the context of exports, with the country needing to adapt to increasingly stringent international standards, particularly European, for food import. It is this scenario that includes the food traceability requirements from the farm (which may be in Brazil) to the end consumer (which may be in Europe, for example). Due to the international requirements for food import, it should be expected that, in the near future, food traceability is going to be a technology adopted in Brazil, especially if we are interested in continuing to export food to developed countries. The sanitary barriers in the developed world are increasingly higher, and not only for food safety reasons, but also for economic reasons, a way of European farmers to restrict competition from foreign food producers. There is no shortage of Brazilian experiences with food traceability, as the initiatives in Santa Catarina and Paraná show. However, in most cases, they are still based on voluntary actions of some communities. There are few initiatives that could be classified as results of public policies, i.e., based on laws and obligations, as it is in Europe, for example. In short, food traceability is a great opportunity. In addition to food safety being a consumer’s right, it is also a need from an economic point of view, given the importance of food in the Brazilian foreign trade.

31 Article agrosoft.org.br/br/cientistas-sugerem-que-doenca-da-vaca-louca-foi-causada-a-partir-de-humanos (In Portuguese).

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STANDARDIZATION OF AGRICULTURAL SOFTWARE Despite efforts made to establish standards for the development of an agricultural software, to date there is no consensus on the subject. The API-AGRO platform emerges as an initiative in this direction. One of the work groups (“D-Work Group”) of project EUNITA (1997), funded by the European Union, was devoted to studying the “impacts of standardization on the development and use of agricultural software.” The final report of the project standards in ICT as follows: "STANDARDS IN ICT Standards are documented agreements containing technical specifications or other precise criteria to be used consistently as rules, guidelines or definitions of characteristics, to ensure that materials, products, processes and services are fit for their purpose32. The documented agreements may be classified as: • Standard: A standard is a document that is published by a standardisation institute, e.g. ISO. A standard is the result of a democratic process. • De facto standard/industry standard: A de facto standard is a widely used agreement, without any involvement of a standardisation institute. The agreement is available to the public. It is not a proprietary/patented standard. Standards may not be (widely) implemented, de facto standards are, by definition, implemented on a wide scale. This report covers both standards and de facto standards. Classification of standards A well-known classification of standards for the integration of information systems is the OSI-model (open system interconnection model). This model distinguishes 7 layers of standardisation: (1) physical, (2) datalink, (3) network, (4) transport, (5) session, (6) presentation and (7) application layer. The Eunita D-Work Group only focuses on standards used in the application/presentation layer." Also according to the final report of project EUNITA, studies were performed in the mid-90s for two specific cases, encopassing the situation in several European countries (France, Germany, The Netherlands and Portugal): 1. xchange of animal data: as an example exchange of data in the milk chain involving farmers, dairy breed associations, veterinarians, animal registration entities, etc., were used at the national and international levels. 2. xchange of field data for precision agriculture: there is a need for standardization, so that the various devices can talk to each other. This was how the standards used by the large multinational companies that manufacture agricultural machinery: seeders, combine harvesters, etc., were studied.

32 Standards in ICT - www.iso.ch/infoe/intro.html.

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Despite the efforts made to establish standards for the development of an agriculture and livestock software, to date, there is no consensus on the subject, which is supported by proposals such as the API-AGRO platform33 (Haezebrouck T.P.; Emonet, E; Siné, M., 2015) thus defined by its proponents: "In accordance to [French] National Program of Agricultural and Rural Development objectives, the project API-AGRO has been selected to improve interoperability and data exchange between agricultural stakeholders. This project is a non-contradictive new oncoming about the previous projects working on agricultural data exchanges trying to standardizing data format to ensure exchange. Based on Application Programming Interfaces (APIs), this emerging digital distribution channel enables data sharing with clear rules of use. APIs can provide a hook for partners or third party developers to access data and services to build applications or to offer new services for different Farm Management Information Systems (FMIS). In the French Ecophyto program, we observed that centralizing data from many different FMIS has shown its limits; all FMIS have their own standards, constraints and goals. This has led to the creation of a new FMIS to receive all the data collected from the farms national networks. In this context, APIs seem to be an innovative solution to expand data exchanges and promote open-innovation. Thus, we made the assumption that pooling data sets produced by different partners, or web services provided by API, will facilitate the design of the new FMIS. We also believe that APIs could boost innovation in the agricultural ecosystem by integrating new players (start-up, for example) promoting co-development of disruptive web applications for e-agriculture. So we designed the API-AGRO platform from an agronomic references inventory existing in each program’s partner. The platform offers unified access to a data set in open or private mode and a set of Open or Private-APIs (reserved for specific partners or clients)."

2.3 AGRICULTURAL SOFTWARE: OFFER Since the 90s, efforts were made to try and identify and classify information on software applied to agribusiness: Farmsoft, in Europe; and the Agrosoft Guide and project Sw Agro, in Brazil, are some initiatives in this direction. Since the 1990s, an effort has been made to try and identify and classify information on vertical business software for the industry, i.e., specifically designed to solve problems in the agricultural sector. In addition to these, the horizontal business software (spreadsheets, word processors, general accounting, ERP, etc.) are also used in all agricultural activities, but are not analyzed in this section. In Europe, the mapping initiative resulted in the Farmsoft catalog, within the scope of the EUNITA project, and in Brazil, in the Agrosoft Guide, both developed in the 90s. Most recently, Embrapa Informática Agropecuária, partnering with various institutions, including Softex, developed the SW Agro project. The results of these initiatives are reported below.

33 API-AGRO: Project: www.api-agro.fr - Paper: bit.ly/AgroTic_ApiAgro - Platform: plateforme.api-agro.fr.

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FARMSOFT The European catalog of agricultural business software named Farmsoft was published for the first time, still as a prototype, in 1991 (Gelb, E., 1997), which was one of the actions of Work Group B from project EUNITA. Representatives from eight countries participated in the project: Germany, Belgium, Spain, France, Portugal, Israel, Italy and The Netherlands. In addition to 1991 version, two other issues were published, in 1994 and 1996. France and Italy launched, respectively in 1983 and 1986, their national catalogs. Table 2.1 shows a summary of data on the supply of agricultural software registered in Farmsoft 94 and Farmsoft 96 catalogs. It is noted that the number of products grew from one issue to the other, with the software in category administration/management accounting for the largest share in both occasions and expanding its share in total from 94 to 96. TABLE 2.1 – CATEGORY-WISE OFFER OF AGRICULTURAL SOFTWARE IN EUROPE – 1994 AND 1996 CATEGORY Administration / Management Animal husbandry Vegetable crops Machinery and process control Irrigation Others TOTAL

1994 300 280 160 80 35 205 1.070

1994 (%) 28,0 26,2 15,0 7,5 3,3 19,2 100,0

1996 513 335 117 84 19 247 1.315

1996 (%) 39,0 25,5 8,9 6,4 1,4 18,8 100,0

Source: Farmsoft 94 & 96 (Gelb, E., 1997).

AGROSOFT GUIDE In 1995, the Agrosoft nucleus of the then Softex program, which is today Softex Association, published the first Brazilian Agriculture Software Guide. The publication was released during Agrosoft 95 held in the city of Juiz de Fora, in Minas Gerais. Two other issues of the Guide were published in 1997 and 1999. Table 2.2 shows a summary of the amount of agricultural software cataloged by Agrosoft in the three opportunities, considering various categories. There was an increase in the number of products over the issues, with the offerings of solutions for cattle grazing deserving some emphasis. TABLE 2.2 – CATEGORY-WISE OFFER OF AGRICULTURAL SOFTWARE IN BRAZIL– 1995, 1997 AND 1999 CATEGORY Cattle grazing Administration/Management Animal nutrition Others TOTAL

1995 26 26 8 35 95

1995 (%) 27,4 27,4 8,4 36,8 100,0

1997 46 34 11 55 146

1997 (%) 31,5 23,3 7,5 37,7 100,0

1999 55 37 13 63 168

1999(%) 32,7 22,0 7,7 37,5 100,0

Source: Agrosoft Guide of Agricultural Software 97 (Munis, G., 1997) and Agrosoft Guide of Agricultural Software 99 (Villela, C., 1999).

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PROJECT SW AGRO EMBRAPA In the late 2000s, with support from various partners, Embrapa Informática Agropecuária conducted a comprehensive mapping of the offer of agricultural business software and promoted a survey of demands on ICT in agriculture with agricultural cooperatives and institutions of Technical Assistance and Rural Extension (TARE). The results of the study were reported in the article SW AGRO: Study of the Brazilian Software Market for Agribusiness (Mendes, C.; Oliveira, D.; Santos, R., 2011). According to the authors: “It were identified 162 private companies developers of rural software, by geographic distribution, size and their 402 software products. Of the 230 rural cooperatives which took part in the survey, 39% use some agribusiness software. Their software demands are for selling agricultural products, farm management and accounting. From the institutions of the TARE, 132 took part in the survey. Among the 162 agricultural software companies (developers or distributors), which took part in the survey, 97% are micro - small, concentrated in the Southeastern and Southern Regions of Brazil. For our study, we split the 402 software products developed/distributed by these companies into four categories covering the various supply chains of agribusiness: (1) administration / management, (2) process control / rural activities, (3) vegetable crops, and (4) animal husbandry. Within the categories, the software products were split into areas of application considering the characteristics, purposes and functions of each software.”

Agricultural software companies In the late 2000s, project Sw Agro identified 162 companies offering agricultural software. These are small businesses concentrated in the Southeastern Region, founded mainly from 96 – 98, the heyday of software initiatives oriented to agribusiness. The 162 companies offering agricultural software identified within the project were geographically distributed as follows: 94 companies (58% of the total) in the Southeastern Region of Brazil, from which 54 in the state of São Paulo and 34 in Minas Gerais. Forty five other companies (27.8%) were based in the Southern Region. Viçosa, in Minas Gerais state, was the city that concentrated the largest number of companies, 11 (6.8% of the total). It is worth recalling that Viçosa is home to the Federal University of Viçosa (UFV), an institution of excellence in education, research and agricultural extension, with programs of support for entrepreneurs that had, from 1994 to 2005, a strong support and funding from the Softex, CNPq and Fapemig program, through the Agrosoft nucleus located in Juiz de Fora (MG). Figure 2.3 shows the distribution of the 162 companies according to the year of foundation. It may be observed that the vast majority emerged in the 90s, especially from 96 to 98. From this period, a decline is observed in the emergence of companies offering agricultural software, with those founded from 2002 to 2007 representing 20.8% of the total surveyed.

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FIGURE 2.3 – DISTRIBUTION OF COMPANIES OFFERING AGRICULTURAL SOFTWARE, SEPARATED BY THEIR YEAR OF FOUNDATION – BRAZIL 25,0%

22,4

20,0% 15,3

15,0%

12,2

13,7

12,2

10,0% 5,0% 0,0%

3,0

6,1

6,1

84-86

87-89

7,1

2,0

Ano 1979 80-83

90-92

93-95

96-98

99-01

02-04

05-07

Source: SWAgro Embrapa, 2010.

Appendix 2.A2 lists the 162 companies that took part in the SW AGRO study, with the trade names by which they are known and their respective website URLs and Facebook pages. Some companies recently identified during the process of review of Embrapa’s original study were also added to the list.

Software products It were identified 402 agricultural software products, a significant portion of which oriented to farm administration/management. Tables 2.3 through 2.6 show the distribution of agricultural software by category and area of application. The total in each table may be greater than the 402 products identified in Embrapa’s survey because the same product can serve more than one area of application, and also belong to more than one category. TABLE 2.3 – SW AGRO EMBRAPA – PRODUCTS IN CATEGORY ADMINISTRATION/MANAGEMENT, SEPARATED BY AREA OF APPLICATION AREA OF APPLICATION Farm management Marketing Input management Accounting Management/maintenance of machinery, equipment Personnel management Lab management TOTAL

PRODUCT 145 88 86 55 47 32 14 467

% (TOTAL) 31,1 18,8 18,4 11,8 10,1 6,9 3,0 100,0

Multiple answers are allowed. Source: Mendes, C. et al, 2011.

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TABLE 2.4 – SW AGRO EMBRAPA – PRODUCTS IN CATEGORY PROCESS CONTROL/RURAL ACTIVITIES, SEPARATED BY AREA OF APPLICATION AREA OF APPLICATION Traceability Fertilizing and liming Surveying/topology Genetic improvement Agronomical prescriptions Plant Health Environmental Management Soils Forest management/reforestation Irrigation Mechanization Post-harvest, processing and storage of product Forest Inventory Veterinary prescriptions Integrated pest management Crop forecast Agrometeorology Agricultural zoning Bioinformatics TOTAL

PRODUCT 61 25 21 20 18 17 15 15 13 13 13 11 11 11 9 5 5 2 1 286

% (TOTAL) 21,3 8,7 7,3 7,0 6,3 5,9 5,2 5,2 4,6 4,6 4,6 3,9 3,9 3,9 3,2 1,8 1,8 0,7 0,3 100,0

Multiple answers are allowed. Source: Mendes, C. et al, 2011.

TABLE 2.5 – SW AGRO EMBRAPA – PRODUCTS IN CATEGORY VEGETABLE CROPS, SEPARATED BY AREA OF APPLICATION AREA OF APPLICATION Sugar and alcohol Soybeans Agroforestry Corn Coffee Eucalyptus Fruit Wheat Cotton Beans Rice Sunflower Vegetables Palm fruit Castor beans TOTAL Multiple answers are allowed. Source: Mendes, C. et al, 2011.

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PRODUCT 21 20 17 16 14 14 13 10 9 7 6 3 3 1 1 155

% (TOTAL) 13,6 12,9 11,0 10,3 9,0 9,0 8,4 6,5 5,8 4,5 3,9 1,9 1,9 0,6 0,6 100,0

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

TABLE 2.6 – SW AGRO EMBRAPA – PRODUCTS IN CATEGORY ANIMAL HUSBANDRY, SEPARATED BY AREA OF APPLICATION AREA OF APPLICATION Beef cattle Dairy cattle Swine Poultry Sheep Buffalos Goats Equine Fish Seafood Bees TOTAL

PRODUCT 53 45 31 19 19 18 18 15 9 6 2 235

% (TOTAL) 22,6 19,2 13,2 8,1 8,1 7,7 7,7 6,4 3,8 2,6 0,9 100,0

Multiple answers are allowed. Source: Mendes, C. et al, 2011.

2.4 SEED ACCELERATORS AND ICT STARTUPS FOR AGRIBUSINESS Project Smart AgriFood, in Europe, a model oriented for the search of open standards; startup FarmLogs, in the US, a proprietary proposal for rural management; and StartUP Brasil, in Brazil, a government initiative. The last EFITA Convention, in 2015, dedicated one of six major sessions to the theme “Future Internet accelerators for agriculture”, highlighting the importance of accelerating startups in ICT for agribusiness, where project SmartAgriFood, a project funded by the European Union, was presented. In the US, startup FarmLogs has been conquering its space, offering an online System of Management of Agricultural Information in a country that is one of the major global food producers. Brazil, which competes on equal terms with the United States in the international food trade, also develops the project StartUp Brasil, where nine startups in ICT for agribusiness are in a process of acceleration of their business plan. We present below a brief exposition of these initiatives which are now at the forefront of business opportunities worldwide, and which, as may be understood, also cover agribusinesses.

EUROPA SMART AGRI-FOOD Project Smart Agri-Food34, funded by the European Union, is described on their website as follows: • Aims to boost the application and use of future internet ICTs in the Agri-Food domain. • Will increase the competitiveness of the European Agri-Food domain. • Will effect a huge number of SMEs in the Agri-Food domain throughout Europe. The following is a brief description of two projects funded by project SmartAgriFood:

34 SmartAgriFood Project: www.smartagrifood.eu

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Pomodore "Within the Pomodore project, Enco S.r.l. and Lesprojekt35 are implementing a Future Internet based application, that will help tomato growers to monitor and analyse key data (temperature and humidity of air and soil) from sensors from their fields in real time. The Pomodore solution will be a decision web-based system using wireless sensors and tags for monitoring air and soil temperature and humidity in real-time, mobile data collection, alarms based on Complex Event Processing and Advanced Visualisation. It intends to support tomato producers to implement proactive and corrective actions, such as the precise harvest time, the optimal irrigation practices, etc, tailored on live data. From the technological point of view the system will be composed from WSN and IoT (FI Ware based solution, FISPACE market place, other FIWARE36 tools supporting easy building of new applications and set of supporting tools (mainly coming from FOODIE project). The system will consist of two groups of Apps: 1. Farming Apps: • • • • •

Client for mobile data collection Alert Apps informing about current situation Data layer Apps allowing to import new farm data Data editing Apps supporting on line data editing Data analysis module allowing also analyses of historical data and supporting tactical decision.

2. Administration Apps: • Supporting user management and invoicing • Parameterization of the system"

Smarthoney "Within the SmartHoney project, Lesprojekt Ltd. is implementing a Future Internet based application, that will help bee keepers to monitor the situation inside and outside the bee hive through the set of sensors, perform analysis in real time and send alerts depending on current conditions. SmartHoney solution is focused on situation monitoring inside and oudside of the hive, providing prediction of bee behaviour and sending alerts to the beekeepers and allowing for interactive analysis in real-time. Monitoring will be based on Wireless Sensors Network and it will be focused on the following: (1) Monitoring Environmental Conditions outside the hive; (2) Weather monitoring for prediction of the behaviour of bee in the hive and prediction of necessary interventions of the breeder; (3) Monitoring Living Conditions inside the hive.

35 Lesprojekt: www.lesprojekt.cz/index_en.html. 36 Fiware: www.fiware.org e www.ccss.cz/en/press-release-fiware-lab.

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We suppose measurement of the following phenomena: • • • • • •

Monitoring the amplitude and frequency of the sound in the hive Measuring the weight of the hive Temperature in the hive Humidity in the hive Movement monitoring on the hive entrance Off season feeding

Analyses: • • • • • • • •

Honey Production Unauthorized manipulation Overheating of the hive (bees cooling) Hypothermia hive (bees heating) Summation of departures and arrivals Time between flight activity Average flight time Differences between the number of arrivals and departures in relation to the time of day.

For monitoring will be used WSN based on IQRF nodes. IQRF is a platform for low speed, low power, reliable and easy to use wireless connectivity e.g. for telemetry, industrial control and building automation. IQRF is a complete system from one brand including hardware, software, development support and services. IQRF network can be easily connected to the Internet via Cloud server. IQRF is ideal platform to implement Internet of Things." Projects Pomodore and SmartHoney are being developed in the leading laboratory in wireless Fiware communication, having Lesprojekt as the partner responsible for hosting the lab servers.

ESTADOS UNIDOS: FarmLogs The standardization of Farm Management Information Systems (FMIS) has been pursued by several groups, pivotally, as in the line of the studies of project EUNITA and subsequent projects, or in the line of projects such as the API-Agro open platform, more flexible and collaborative. In parallel, startups have tried to establish a market standard by offering an online service called “cloud computing" at no charge for a basic level usage and with charges incurred for heavy users. For example, American startup FarmLogs37 has already raised US$ 15.83 million38 from eight venture investors in five rounds of negotiations since 2012. Basically, it offers a Farm Management Information Systems using cloud computing for storage and processing data and information; use of data mining techniques to extract knowledge; and precision agriculture hardware and software to capture data and operate in the application of agricultural inputs directly on crops in the field. 37 FarmLogs - Website: www.farmlogs.com. 38 FarmLogs - Investors: www.crunchbase.com/organization/farmlogs.

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FarmLogs offers three levels of use39 of their services: (1) Standard: free; (2) Advantage: from US$ 500 per year; and (3) Prescriptions: from US$ 500 per year, plus US$ 8.50 per acre monitored. The company is rated by websites specializing in high-tech business as operating in the fields of software, agribusiness and big data, i.e., it collects and analyzes massive agricultural data aiming to classify information and extract knowledge to help solve farmers’ and their customers’ problems. It is still too early to say whether this business model in ICT for agribusiness will actually succeed and be sustainable. In case it is indeed consolidated as a reference brand in the market of agricultural information management, it will have established a new and promising paradigm that could change, once and for all, how farmers manage their businesses, and also, for the first time, allow the emergence of a big player competing in ICT for agribusiness in a global scale. If FarmLogs does consolidate its name into the market as a proprietary standard for systems of management of agricultural information, they may eventually end up seeing a competitor grow with a different proprietary standard or they may compete with an open standard which they can impose through the collaboration of a large number of small players worldwide. In this case, French open platform API-AGRO, for example, might be a starting point. As it is in the world of high tech businesses, in order for you to succeed, it is critical that you are perceived as a big business in the future, and FarmLogs has been doing their homework, as shown in some video reports40 that circulate in the American trade press, interviewing Jesse Vollmar, founder and CEO of FarmLogs. FIGURE 2.4 – JESSE VOLLMAR, CEO OF FARMLOGS, WITH FOX BUSINESS NETWORK

BRASIL: StartUP BRASIL “StartUP Brasil, a National Program for Seed Acceleration of Startups, is an initiative of the federal government created by the Ministry of Science, Technology and Innovation (MCTI) managed by Softex, partnering with seed accelerators to support technology-based emerging companies, the startups. The startups meet their purpose of continuously revitalizing the market, but they need a proper environment to develop and succeed. The figure of the seed accelerator emerges in this context as a strongly market-oriented agent, usually from the private initiative and with good capacity for financial investments, which works to guide and enhance the development of startups. 39 FarmLogs - Plans: go.farmlogs.com/pricing. 40 FarmLogs - Video reports: bit.ly/AgroTic_FarmLogsReport1 and bit.ly/AgroTic_FarmLogsReport2.

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StartUP Brasil is part of TI Maior – a Strategic Software and IT Services Program, which, in turn, is one of the actions of the National Strategy for Science, Technology and Innovation ENCTI, which elects the ICTs among the priority programs to boost the Brazilian economy.” (transcript from the StartUP Brasil41 project website). Nine companies of project StartUP Brasil have plans focused on the development of ICT applications for agribusiness. Below are some information contained in StartUP Brasil project website about these companies and their business. Agrosmart42 “Agrosmart believes they can improve the lives of people in the field by pursuing a smarter way of improving productivity and optimizing the use of resources in agriculture. To this end, they created smart farming, connecting the farmer to his crops. Thus, by monitoring more than ten environmental variables, they generate relevant information to assist producers in their decision making process. Using sensors, meteorological data, image processing and an online application, the farmer can monitor several variables in real time for precision agriculture. Using this solution, they help their customers improve the management of their agribusiness, and understand their crops and their needs at every moment, in relation to irrigation, pests and diseases.”

Algrano43 “Did you know that coffee producers usually earn only 7% of coffee’s retail value? To counter the increase in costs and low prices, producers are seeking product differentiation by increasing the quality of coffee. They want to add value to their product. On the other hand, small roasters of specialty coffees are popping up all over the major cities and around the world. They want to ensure high quality and unique coffee blends, as well as maintaining a direct relationship with producers. Considering this, Algrano has been building a common online market to connect coffee producers directly with coffee roasters worldwide. Algrano empowers producers by offering them an easy and efficient way to market their coffee directly to roasters, and at the same time, by allowing roasters to access a wide range of different coffee coming straight from farms.”

41 StartUP Brasil: www.startupbrasil.org.br 42 Agrosmart: www.agrosmart.com.br 43 Algrano: www.algrano.com

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BovControl44 “BovControl is a tech startup that is revolutionizing the way of managing the livestock activity worldwide. We empower farmers with reliable information, possible through mobile (cell phones) collection of data, stored in the cloud and available through an online panel.”

E-Aware45 “E-Aware Technologies develops wireless sensing and communication technologies, allowing access to information of industrial processes in a reliable, safe, quick and easy way. The technology is applied in different segments, such as industry, agriculture, livestock, health and others. By applying concepts of the Internet of Things (IoT), we make it possible to obtain information in hard to reach locations, allowing connectivity between the different players in the process, integrating and analyzing data in real time. The solutions offered enable decisions with agility and greater knowledge concerning the progress of the process, and ensuring a wide view to increase efficiency, safety and reduction of losses.”

Gecam46 “Gecam is a system for aquaculture composed of multi-parameter buoys able to monitor the levels of the physical-chemical variables of the water in order to enhance the productivity of crops through a continuous and efficient control, providing a favorable environment to production control, reducing the risks of low immunity of underwater life forms. In addition, it allows the client to remotely monitor the level of parameters such as dissolved oxygen, ammonia, pH and temperature, as well as enabling the automatic control of the dissolved oxygen level.”

44 BovControl: www.bovcontrol.com 45 E-Aware: www.eaware.com.br 46 Gecam: www.zigtec.com.br

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Kajoo47 “Project Egg++ consists in creating software to count and rate products in production lines, based on computer vision. Despite the application of industrial cameras in some production processes not being anything new, Egg++ is unique because of their algorithms, software architecture and equipment topology, which allow the transformation of a micro-processed camera 100% manufactured in Brazil and approved by ANATEL costing something around $ 300~400 Reais at retail into an industrial strength sensor. By creating this technology, Egg++ enables counting and imaging classification in industrial sectors which could never be met due to high costs and to the not scalable model of the solutions.”

Pastar48 “Created in 2013, project Pastar aims to reduce the impacts caused by seasonalities in animal breeding processes through a set of online systems capable of articulating transactions of leasing of pastures and marketing of animals. Currently, the company runs two supporting lines in sales: “Classificado Pastar" (Pastar Ads) the world’s first application for livestock sales, a solution tailor-made to meet every producer need, and the second solution is the marketplace “Pastar”, the world’s first in managing marketing and leasing of pastures, which also includes the animal’s trading validity. With their solutions, Pastar is able to meet all levels of producers safely, quickly and efficiently.”

Rogue Rovers49 “Rogue Rovers, LLC, based in Ashland, Oregon, United States and Brazil developed the prototype of a semi-autonomous, electrical vehicle for all types of terrain called FarmDogg™, which is being designed in an on-board data collection platform for remote data collection and surveillance of crops, as well as for autonomous navigation. Being the primary market, Rogue Rovers is focusing on specialized farms, such as orchards of grape crops for wine production and livestock.”

47 Kajoo: www.kajoo.com.br 48 Pastar: www.pastar.com.br 49 Rogue Rovers: www.roguerovers.com

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Smart Agriculture50 “Smart Agriculture Analytics (SAA) is a SaaS platform for customer acquisition in the agricultural technology industry. The system allows small and medium enterprises, who sell the best “agritech” in the world, to identify, track and connect with appropriate Chinese customers and compete successfully in the world’s largest market. The company started in China and came to Brazil aiming at expanding the cataloging of the demand in growing markets, starting in the states and going national so that the Smart Agritec industry succeeds in meeting the specific needs of farmers. SAA has data processing tools in several languages, access to local, respected databases, and a team of experts working to deliver reliable leads whenever customers need them.”

FINAL THOUGHTS Since the 80s, with the first microcomputers going into the market, Information and Communication Technologies (ICT) are envisioned as a great opportunity for a new boost to the Green Revolution in agribusiness. It was imagined that, with the versatile microcomputers more accessible to people’s pockets, the set of new technologies based on bits and bytes could make the knowledge of agricultural sciences deposited mainly in universities and research institutes reach farmers in an easier and quicker way. In the socalled “agricultural universities” around the world, either at Purdue or in Urbana-Champaign in the US; or in Wageningen, in The Netherlands; or in Viçosa, or Lavras, or Piracicaba in Brazil, for example, researchers invested their precious time and resources in this effort. In the 90s, it was the time of precision agriculture to renew the hopes of the so-dreamed embrace between the farmer and the computer. After all, who does not want to “remain lying on a hammock or a bed, with our garden hoes moving by themselves to weed the fields, and the sickles will harvest by themselves, and the car going by itself to pick up the harvest”? Riobaldo’s dream in Guimarães Rosa’s “Grande Sertão: Veredas”, a prophetic vision of the use of robots in agriculture, imagined at least forty years before they started thinking the real possibility of this actually happening. Fair enough that Brazilians bestowed this great writer with the title of "Patron of the Brazilian Precision Agriculture”. And in the brink of the twenty-first century, it was the telematics’ turn, the Internet was reawakening the dream that this great network had everything to do with the business, and therefore, with agribusiness, since this is a big business. All this academic and entrepreneurial enthusiasm with the use of ICTs in agribusiness had its heyday in the 90s.It was back then that most ICT suppliers foragribusiness were created, and also the major organizational initiatives (Agrosoft, SBIAgro, Eunita, Efita, Afita, etc.) around the world to gather skills and interests in discussions on ‘the computer in agriculture’.

50 Smart Agriculture: www.smartaganalytics.com

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Unfortunately, in none of these times, the ICTs managed to come close to the importance in agribusiness of the other technologies (agricultural mechanization, agrochemicals and genetically improved or modified seeds) that made the Green Revolution. Why is that? Simple. The use of ICTs did not manage to cause an increase in productivity and profitability in the agricultural economy, as was the case with the use of other technologies. What the farmer does not want is more work to use a new technology, however modern it may seem, to earn just a little more money. In ICT applied to agribusinesses, there is no successful company like a John Deere for agricultural machinery, or a Monsanto for genetically improved or modified seeds, or a Bayer for agrochemicals. In short, for some, the situation may seem disappointing, because, after all, “farmers have not yet given the ICTs their proper recognition in their business.” But it can also be perceived as a huge opportunity for something that nobody nowhere in the whole world has discovered exactly how to do. The first who finds out, will be handsomely rewarded, because, as we know, “the winner takes it all”. And that’s exactly when the world of (risky) businesses is in a race to the pot of gold. It seems to be the moment of the startups of ICTs in agribusiness, exploring the technological opportunities of versatility and mobility of applications (apps) for smartphones, of the precision agriculture with their drones, and of the systems of management of agricultural information, such as an online service in the cloud. Large food consumers and importers, the Europeans launched project SmartAgriFood with EU funding, of seed acceleration of the agribusinesses of their startups in ICT. The French rely on the open and collective construction of the API-AGRO platform to perhaps encourage hundreds of micro and small entrepreneurs in establishing an open standard for managing agricultural information. Americans, who produce and export much of the food consumed in the world, bet their chips on the entrepreneurial nature of their young, talented and ambitious entrepreneurs and in the excess of venture capitals. There are killer initiatives underway, such as the one from startup FarmLogs, which has been advancing strongly in search of their space, aggressively offering its System of Agricultural Information Management, such as an online service in the cloud. The price, for the American farmer, of the most basic level of the service offered by FarmLogs is: z-e-r-o. The goal of FarmLogs, with this aggressive marketing strategy, is to establish a market standard in the industry, occupying the space quickly. And mainly, it seems, to form a huge database of American agriculture that will obviously have its value when you think of another business that is doing great: big data. No wonder FarmLogs is also rated, by the specialized sites, as a player in the big data businesses. In Brazil, the federal government and the associations of support to the software and IT services industry launched StartUp Brasil, a National Program for Seed Acceleration of Startups, managed by Softex. Inside it, nine startups focused on agribusiness seek to put the country at the forefront of this business segment. Being a country that is a large exporter of food in natura (soybeans, coffee, corn, meat, etc.), and in view of this trend of venture capitalists assailing resources in this area, the chances of Brazilian startups are good. However, the competition in this area is fierce. And if a major global player of ICT in agribusiness emerges, as is being sought, the chances of our companies will be limited, once again in history, to market niches.

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Thus, it is recommended that Brazil should have a public government initiative to encourage either the launch of a startup with ambitions of being a major global player of ICT in agribusiness, or to promote the development of an open platform with participation of tens or hundreds of startups, in a process of collective construction of business opportunities. If the decision taken is through the collective construction through a consortium of a business environment based on an open standard, the French proposal of an API-AGRO platform could be a good starting point. The initiative may even take advantage of the fact that Brazil is a food exporter and that Europe is an importer of safe agricultural products, from the point of view of their citizens’ health, and socially and environmentally responsible, for the good of mankind.

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2.A1 - ARTICLES ON MOBILE APPS FOR AGRIBUSINESS 1. CNA CRIA APLICATIVO PARA GESTÃO DE RISCO NO AGRONEGÓCIO (CNA DEVELOPS AN APP FOR RISK MANAGEMENT IN AGRIBUSINESS) www.agrosoft.org.br/br/cna-cria-aplicativo-para-gestao-de-risco-no-agronegocio As of January 2015, farmers will be able to use their cell phones to report to the Brazilian National Confederation of Agriculture and Livestock (CNA) cases of property trespassing, weather events, infrastructure problems and issues related to agricultural protection. The information inserted in Application CNA BRASIL will strengthen the Monitoring Centre of Legal insecurities in the Field, of the CNA Institute. 2. NOVA VERSÃO DO APLICATIVO SUPLEMENTA CERTO JÁ ESTÁ DISPONÍVEL (NEW VERSION OF APP “SUPLEMENTA CERTO” IS NOW AVAILABLE) www.agrosoft.org.br/br/nova-versao-do-aplicativo-suplementa-certo-ja-esta-disponivel The purpose of the application is to assist the farmer in evaluating the cost-effectiveness of alternatives of herd supplementation during the dry season. The intuitive interface allows comparisons of supplementation products available to farmers in low-budget supplementation systems, using proteinated minerals, or when there is greater investment, using semi-confinement concentrates. 3. FAO LANÇA NOVO SOFTWARE PARA MONITORAR SITUAÇÃO DAS FLORESTAS NO MUNDO (FAO LAUNCHES NEW SOFTWARE TO MONITOR SITUATION OF FORESTS IN THE WORLD) www.openforis.org www.agrosoft.org.br/br/fao-lanca-novo-software-para-monitorar-situacao-das-florestas-no-mundo The United Nations Food and Agriculture Organization (FAO) has launched new software to help supervise the conditions of forests in the world. The program called Open Foris was launched in Salt Lake City, in the United States, and should be tested in over ten countries in Africa, Latin America and Asia. 4. PRODUTORES CRIAM APLICATIVO QUE AJUDA NO GERENCIAMENTO DO REBANHO (PRODUCERS DEVELOP AN APP TO HELP HERD MANAGEMENT) www.agrosoft.org.br/br/produtores-criam-aplicativo-que-ajuda-no-gerenciamento-do-rebanho The program for mobile devices such as phones and tablets is unprecedented in the country. 5. EMATER-MG LANÇA APLICATIVO COM SERVIÇOS PARA SMARTPHONE E TABLET (EMATER-MG LAUNCHES AN APP WITH SERVICES FOR SMARTPHONES AND TABLETS) www.agrosoft.org.br/br/emater-mg-lanca-aplicativo-com-servicos-para-smartphone-e-tablet Emater-MG invests in new technologies and seeks to increasingly approach their target audience. It is a quick and easily-accessible alternative to obtain information about the company, technical advices and much more. It is an alternative to approach the field and facilitate dialogue with society.

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6. AGRES LANÇA CONCEITO INÉDITO DE TABLET PARA AGRICULTURA E ESPERA CRESCER 30% EM 2015 (AGRES LAUNCHES A UNIQUE CONCEPT OF TABLETS FOR AGRICULTURE AND EXPECTS TO GROW 30% IN 2015) www.agrosoft.org.br/br/agres-lanca-conceito-inedito-de-tablet-para-agricultura-e-espera-crescer30-em-2015 “By introducing our handheld virtual terminal, we started integrating, into a single product, the control needs for machines embedded in cabins, with the flexibility of using the same equipment out of it, like a personal assistant for agricultural use which can complement the function of the computer in the farm office”, explains the CEO, Rafael Klein. In the company’s line of technological solutions are innovations that include monitoring throughout the precision agriculture cycle, namely: Virtual Guide, Autopilot, Spray Controller, Fertilizer Distributor, Planting Controller and Harvest Monitor. 7. APLICATIVO GRATUITO REDUZ CUSTOS NA AGRICULTURA (FREE APP REDUCES COSTS IN AGRICULTURE) www.agrosoft.org.br/br/aplicativo-gratuito-reduz-custos-na-agricultura In 2013, reading an American magazine of agricultural techniques, Branco discovered FarmLogs – farmlogs.com – a free farm management app developed in Ann Arbor, Michigan, in the Midwest of the United States. Since then, the farmer has in his smartphone the data of planting and harvesting of his crops and updated information on prices of commodities on the Chicago exchange. 8. CLIMATE CORPORATION www.climate.com Understand yield-limiting factors by comparing critical farm data layers. Monitor your progress with digitally-displayed data as you pass through the field. Seamlessly collect and send your data from the cab to the cloud. Easily share your farm data with trusted advisors. 9. APLICATIVO AUXILIA DESENVOLVIMENTO DE PROJETOS COM BIOGÁS (APP HELPS PROJECT DEVELOPMENT WITH BIOGAS) www.agrosoft.org.br/br/aplicativo-auxilia-desenvolvimento-de-projetos-com-biogas To help farmers analyze the feasibility of projects related to the production of biogas aimed at power generation, technologist in biofuels Pedro Chamochumbi developed the “Biogas Simulator” app In startup CH4. The company is hosted in Esalqtec, the technological incubator at Escola Superior de Agricultura Luiz de Queiroz (Esalq) of the University of São Paulo (USP), in Piracicaba. 10. AGRITEMPO MÓVEL - SISTEMA DE MONITORAMENTO AGROMETEOROLÓGICO MÓVEL (AGRITEMPO MOBILE - MOBILE AGROMETEOROLOGICAL MONITORING SYSTEM) www.embrapa.br/busca-de-produtos-processos-e-servicos/-/produto-servico/2109/agritempomovel---agritempo-movel---sistema-de-monitoramento-agrometeorologico-movel Mobile app Agritempo allows users to access online meteorological and agrometeorological information from various municipalities and states in Brazil. In addition to reporting the current climate situation, the system database supports the development of recommendations of the Agricultural Zoning of Climate Risks (AZRC), a policy maintained by MAPA.

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11. APLICATIVO LOCALIZA ÁRVORES FRUTÍFERAS (APP FINDS FRUIT TREES) www.agrosoft.org.br/?v=lCtPskN60Tc On one side, nature, and on the other, the smartphone. Realities which could be distant from each other, but are joined by technology. Now the possibility of finding fruits in trees is in the palm of your hand. It was with this idea that three students from the University of Brasilia (UnB) developed mobile application FruitMap. 12. APLICATIVO GRATUITO ENSINA A FAZER E CULTIVAR HORTAS EM CASA (FREE APP SHOWS HOW TO MAKE AND GROW VEGETABLE GARDENS AT HOME) www.agrosoft.org.br/br/aplicativo-gratuito-ensina-a-fazer-e-cultivar-hortas-em-casa A startup from Portugal took advantage of the technology to make life easier for those who want to grow a vegetable garden at home. App “Plantit – Horta em Casa” provides information and practical tips on the biological cultivation of arugula, cilantro, parsley and strawberry, among 28 varieties of vegetables, fruits and aromatic herbs. 13. EVAPOWEB: APLICATIVO DESENVOLVIDO NA UFLA AUXILIA CÁLCULO DA EVAPOTRANSPIRAÇÃO (EVAPOWEB: APP DEVELOPED IN UFLA HELPS THE CALCULATION OF EVAPOTRANSPIRATION) www.agrosoft.org.br/br/evapoweb-aplicativo-desenvolvido-na-ufla-auxilia-calculo-da-evapotranspiracao The manual calculation of evapotranspiration involves many variables, making the process complex and laborious. Thinking about it, a team from the Federal University of Lavras (UFLA), with two graduation students and two teachers, developed EvapoWeb, an app which estimates the Evapotranspiration of reference (ETo) through meteorological data of the region of interest. 14. APLICATIVO DE FISCALIZAÇÃO AMBIENTAL INTEGRADA NO AMAZONAS (APP FOR INTEGRATED ENVIRONMENTAL SURVEILLANCE IN AMAZONAS) www.agrosoft.org.br/?v=8gN_CNLdL78 Amazonas may be the first State to have an app of integrated environmental surveillance. 15. CH4 AGROENERGIA - PLATAFORMA CH4 www.ch4agroenergia.com Application Plataforma CH4 is a solution developed to meet the need of data and technical information referring to the production of biogas. The app offers videos, downloadable material and updated content on biogas and aims to solve problems directly linked to the lack of information of farmers about the potential for energetic use of animal waste and the countless associated benefits. It also helps reducing the time spent searching for several suppliers to make up the list of equipment needed for efficient use of bio-digesters, engines, piping and systems of distributed generation of power.

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2.A2 – LIST OF SOFTWARE SUPPLIERS FOR AGRIBUSINESS – EMBRAPA SURVEY (SW AGRO, 2009) PARTICIPANTS

74

#

COMPANY

UF

WEBSITE

1

A.S. Computadores

SP

www.ascomputadores.com.br

2 3 4 5 6 7 8 9

ABIG Adubar Aequalis aFHF Agran Agriaf Agricerto Agriness

PR BA SP SP SP SP SP SC

www.abigs.com.br www.adubar.com www.aequalis.com.br www.afhf.com.br www.agran.eng.br www.agriaf.com.br www.agricerto.com.br www.agriness.com

10

Agrisoft

SP

www.agrisoft.com.br

11 12 13 14 15 16 17 18

Agroinova AgroJuris Agromanager Agropalmtop Agroprecisa AgroSoft RS Agrotec Agrotecno

SP MG SC PR SP RS RS PA

www.agroinova.com.br www.agrojuris.eng.br www.agromanager.com.br/pt www.agropalmtop.com www.agroprecisa.com.br www.agrosoftrs.com.br www.agrotec.etc.br www.agrotecno.com.br

19

Agrotis

PR

www.agrotis.com

20

Alfa Design

MG

www.alfadesign.eti.br

21

Allcomp

RS

www.allcompgps.com.br

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Apidae Aquisis Arsoft Arvus Assiste Athena Sistemas Ativa Sistemas Automatize Avecom Biosalc BMA Brasil Brasil Agri-Business BrazSoft Brisa Constulting Carpos CDS Software Char Point Checkplant Cientec

MG PE PR SC SP MG PR SC MG SP SP MG MT PR PE PR SP RS MG

41

Cliqsolo

MG

www.apidae.com.br www.aquisis.com.br www.arsoft.com.br www.arvus.com.br www.assiste.com.br www.athenasistemas.com.br www.ativasistemas.com.br www.automatize.net www.avecom.com.br www.biosalc.com.br www.bma-brasil.com www.bbusiness.com.br www.brazsoft.com.br www.brisaconsulting.com.br www.carpos.com.br www.cds-software.com.br www.charpointer.com.br www.checkplant.com.br www.cientec.net http://ultradownloads.com.br/ download/Cliqsolo/

FACEBOOK www.facebook.com/ ascomputadoresaracatuba

www.facebook.com/AgriafSoftware www.facebook.com/agriness www.facebook.com/AgrisoftTiagro-132249716953852

www.facebook.com/ AgrotisAgroinformatica www.facebook.com/alfadesignmg www.facebook.com/allcomp. geotecnologiaeagricultura

www.facebook.com/AvecomSistemas

www.facebook.com/BrazsoftSwRural

www.facebook.com/cdsinformatica

www.facebook.com/cientecvicosa

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

# 42 43 44 45 46 47 48 49 50 51 52 53 54

COMPANY Compu-Soft Controlsoft COSS Consulting CRIA DAP Florestal Datacoper Dataflow Diretórium Domit DZampiér Edata Enalta Engemap

UF SP MT SP SP MG PR SP CE PR RJ SP SP SP

WEBSITE www.compusoft-info.com.br www.controlsoft.com.br www.cossconsulting.com www.cria.org.br www.dapflorestal.com.br www.datacoper.com.br www.dataflow.inf.br www.diretorium.com.br www.domit.com.br www.dzampier.com.br www.edata.com.br www.enalta.com.br www.engemap.com.br

FACEBOOK

55

Etica TI

SC

www.etica-ti.com.br

56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

EVN Falker Fastwave FCA Tecnologia Feedback Fhonline Fórum Access Frigo Data GAtec Geagri Gemini Sistemas Geoexplore Geojá Greentech Informática Grupo Intec Harv Soluções Hotup

SP RS SP PE MG PR SP MS SP SP MG MG SP PR MG DF PR

73

HTM

SP

74 75

i9campo Icase

GO MT

76

Ideagri

MG

77 78

Iepec ILab-Sistemas

SC SP

79

Imagem Geosistemas

SP

80

Inflor

ES

81 82 83 84

Integra Software Invit iPlanus Irriger

SP MG MG MG

www.metrica.com.br www.falker.com.br www.fastwave.com.br www.fcatec.com www.feedbackari.com www.fhonline.com.br www.facebook.com/fhonlinesistemas www.forumaccess.com.br www.frigo-data.com.br www.gatec.com.br www.facebook.com/gatecbrasil www.geagri.com.br www.geminisistemas.com.br www.geoexplore.com.br www.geoja.com.br www.greentechinformatica.com.br www.grupointec.com.br www.harvsolucoes.com.br www.hotup.com.br www.facebook.com/HTM-Gestão-ewww.htm.com.br Tecnologia-141619869240936 www.i9campo.com.br www.icase.com.br www.facebook.com/pages/ www.ideagri.com.br Ideagri/326446690713555 www.iepec.com www.ilab.com.br www.facebook.com/ www.img.com.br ImagemInteligenciaGeografica www.facebook.com/ www.inflor.com.br InflorConsultoriaESistemas www.integrasoftware.com.br www.invit.com.br www.iplanus.com.br www.irriger.com.br www.facebook.com/Irriger

www.facebook.com/CRIA.Campinas www.facebook.com/dapflorestal www.facebook.com/datacoper

www.facebook.com/engemap www.facebook.com/etica. rastreabilidade

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CHAPTER 2 - ICT IN AGRIBUSINESS: TECHNOLOGICAL TRENDS AND BUSINESS OPPORTUNITIES

76

# 85 86 87 88 89

COMPANY ITprovider JetBov JH Consultores JRC Informática Kacique Sistemas

UF MT SC MG SE MT

WEBSITE www.itprovider.com.br www.jetbov.com www.io.inf.br www.jrcinformatica.com.br www.kacique.com.br

90

KM&M

PR

www.kmm.com.br

91 92 93 94 95 96 97 98

Korth RFID Lancecom Landsoft Lidaweb LinkCom Macrosystem Magistech Maxicon Sistemas

SP RS MG PR MG ES MG PR

99

Mega Sistemas

SP

100 101 102 103 104 105 106 107 108 109 110 111 112 113 114

Megasol Metasis Metta Info MIK Brasil Momento Consultoria MRB Agronet Multsoft Net-Fit Novaterra Geo Obers Optimal Paraná Sistemas Paripassu Pic Informática Planejar

PR SC SP SP ES MG GO SP RJ MG SP PR SC SP RS

115 Portal do Agronegócio

MG

116 Portal Ruralsoft

MG

117 118 119 120 121 122 123 124 125 126 127 128

PR RS PE SP MG MT SP MG RS MS PR SP

www.korth.com.br www.lancecom.desenvolve.com.br www.landsoft.com.br www.lidaweb.com.br www.linkcom.com.br www.facebook.com/Linkcomsistemas www.macrosystem.com.br www.magistech.com.br www.maxiconsistemas.com.br www.facebook.com/ www.mega.com.br megasistemascorporativos www.megasol.com.br www.metasis.com.br www.facebook.com/Metasis www.mettainfo.com.br www.mikbrasil.com.br www.momentoconsult.com.br www.gaagrosolucoes.com.br www.multbovinos.com.br www.net-fit.net www.novaterrageo.com.br www.facebook.com/novaterrageoface www.obers.com.br www.facebook.com/obersconsult www.optimal.com.br www.paranasistemas.com.br www.paripassu.com.br www.picinfo.com.br www.planejar.com www.facebook.com/Portal-dowww.portaldoagronegocio.com.br Agronegócio-216394215107240 www.facebook.com/Portalwww.ruralsoft.com.br RuralSoft-360161574032172 www.prismainformatica.com.br www.profruta.com.br www.procenge.com.br www.facebook.com/Procenge www.procion.com www.prodap.com.br www.facebook.com/PRODAP.agro www.prodix.com.br www.facebook.com/agrotitan.viasoft www.proxima.agr.br www.rcs.srv.br www.retta.com.br www.rivieratecnologia.com.br www.sag.com.br www.santiagoecintra.com.br www.facebook.com/SCGeotecnologia

Prisma Informática Pró-Fruta Procenge Prócion Prodap Prosoftware Próxima RCSNet Informática Retta Riviera SAG Santiago & Cintra

FACEBOOK www.facebook.com/jetbov

www.facebook.com/kacique.sistemas www.facebook.com/KMM.Engenharia. de.Sistemas

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

# 129 130 131 132

COMPANY Scadi Agro Seiva Brasilis Siagri Silviconsult

UF RS SP GO PR

WEBSITE www.scadiagro.com.br www.seivabrasilis.com www.siagri.com.br www.silviconsult.com.br

133 Sira

SP

www.sira.com.br

134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149

SP MS SP MG SP MT PR SP MG RS PR MG RS SP RJ SP

www.softfacil.com.br www.softgran.com.br www.softcomex.com.br www.procreare.com.br www.solsoft.com.br www.soscpd.com.br www.spro.com.br www.sugarsoft.com.br www.suinsoft.com.br www.sulsoft.com.br www.tavol.com.br www.tdnet.com.br/tds www.tecniagro.com.br www.teodonivel.com.br www.threetek.com.br www.tibrazil.com

150 TIAgro

SP

www.tiagro.com.br

151 152 153 154 155 156 157 158 159 160 161 162 163 164 165

SP SP MG SP MG RS DF SC SP MG MG MG SC PR SP

www.toledobrasil.com.br www.totvs.com.br www.treesoftware.com.br www.unisoma.com.br www.valeverde.com www.vectis.com.br www.ecapataz.com.br www.softwareveterinario.com.br www.vifbrasil.com.br www.vilesoft.com.br

Softfacil Softgran Softway Solides Solution Soscpd Spro IT Solutions Sugarsoft Suinsoft SulSoft Tavol TD Software Tecniagro Teodonivel Threetek TI Brasil

Toledo do Brasil Totvs Treesoftware UniSoma Vale Verde Vectis Verbis VetSoft Vif Brasil Vilesoft Vipper Winfit WK Sistemas Xtrategus Zkitta

www.winfit.com.br www.wk.com.br www.xtrategus.com.br www.zkitta.com.br

FACEBOOK

www.facebook.com/Siagri www.facebook.com/SiraSolutions-353498058062044 www.facebook.com/softgra www.facebook.com/procreare

www.facebook.com/threetek www.facebook.com/AgrisoftTiagro-132249716953852

www.facebook.com/vetsoft

www.facebook.com/WKSistemas

Source: Sw Agro Embrapa (2011), with own preparation in the update and addition of data.

77

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

CHAPTER

3

PROSPECTS AND PREDICTIONS FOR ICT IN BRAZILIAN AGRIBUSINESS INTRODUCTION Chapter 1 introduced a typology that considers opportunities for the operation of companies in the value chain of software and ICT services, including the following options: software embedded in devices, machinery and equipment; communications infrastructure; middleware and infrastructure software; and horizontal and vertical applications. The conclusion is that the major global players dominate the value chain links where there are greater possibilities for offerings of generic solutions which can be used in different sectors/economic activities, as it is the case with the infrastructure software. For these large companies, operating in the agribusiness sector would be another opportunity to expand their businesses and scaled gains. The expansion effort would be minimal because it would require the delivery of little modified or not-at-all modified solutions. New entrants, generally small- and medium-sized companies, are more likely the greater the specificity of their solutions and the more they turn to market niches not yet taken by large suppliers of generic solutions. However, the more specific your solutions are, the less chance they have to boast scaled gains, which tends to derail the deal or require greater efforts to readjust solutions to other areas of application. Specialization, therefore, can produce the side effect of reducing the market size, inhibiting the company’s growth. Despite the strength of the Brazilian agribusiness, the use of ICTs in the sector, scaled through the presence of professionals with occupations in ICT, it is still restricted to a few production chains and larger rural establishments. Thus, in Brazil, there is still a lot to do with regard to the computerization of agribusiness, which beckons with opportunities for companies interested in providing solutions to the sector. Some of the reasons that explain the low adoption of ICTs in agribusiness, especially in agriculture and livestock, are discussed in Chapter 2. One of them has to do precisely with the fact that the solutions available in the market do not meet the farmers’ needs. They are generic, complex and far from the reality implemented of management and control of processes by the vast majority

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of farmers. The use of the solutions available on the market require the farmer to adapt their traditional way of doing things, which leads to a poor cost-effectiveness. Something which can make all the difference in the use of ICTs in agribusiness (AgroTIC) is the adoption of business models capable of stimulating the use of new technologies and, at the same time, inhibit the obstacles to their adoption. The new business models, not the technologies themselves, can pave the way for the adoption of the AgroTICs. Instead of technologies being made available to farmers in the form of products to be purchased and handled, they can be offered as services provided by third parties, with a more satisfactory cost-effectiveness. This Chapter takes up the discussion of the new scenario brought about by the emergence of new technologies and alternative business models. It also discusses how the new reality in construction can profoundly change the logic of the value chain of software and ICT services for agribusiness which, in general, so far, has focused, due to scaled gains, on the search for generic solutions instead of specialized solutions. The participation of startups in the new context will be considered. The possibilities of development of the AgroTICs will also be explored, considering alternative reference models already mentioned in Chapter 2. One of the models suggested is grounded in the emergence of a proprietary leading platform, strong enough to create significant lock-in effects in the AgroTIC market. The other model is based on the adoption of an open standard platform, capable of, under certain conditions, mitigate the lock-in effects. Both models may allow the inclusion of a relatively large number of companies specializing in the AgroTIC ecosystem, but may also inhibit the entry of new players in the market. The cards are on the table, the future is still uncertain.

3.1 THE NEW CONTEXT OF ICTs New driving forces: mobility, cloud computing, social media, big data, physical & digital convergence and Internet of Things. The emergence of digital moments. A few years ago, Gartner consulting has been drawing attention to four forces/trends that, operating together, may cause a very significant impact on the ICT industry and in the way people work and live. These forces are mobility, cloud computing, social media and big data/analytics. The company has recently expanded their vision, gathering to the forces already identified two new trends considered highly impactful: the strong convergence between the physical and the digital worlds, and the advent of the Internet of Things (IoT). All these various forces combined will favor the emergence of digital moments, i.e., the combination of processes, things and people, intensively supported by the new technologies, to meet a given need, which emerges in a given context. Many of the processes involved in digital moments, triggered and fed back by ‘things`, are transparent to those who take part in them, i.e., are pervasive, seem natural, happen without human intervention. Figure 3.1 provides an example of the processes occurring in a given digital moment. A smart house senses that, due to problems caused by infiltration, it should be repainted. In parallel, it receives reports from the washing machine that it`s necessary to buy washing powder. It communicates with the home owner to obtain the authorizations for painting and bying the cleaning product. As of the authorization, a whole process based on communication between things is triggered, and the result

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

is that the house ends up painted and the cleaning product ends up bought. This is just one example of digital moments that dominate the daily lives of people such as the birthday of a friend, the theft of a car, the cancellation of a trip, the planting of corn, the harvest of the coffee, the vaccination of the cattle, etc. FIGURE 3.1 – GARTNER EXAMPLE: THE HOUSE UNDERSTANDS IT NEEDS PAINTING

BEGINNING OF THE PROCESS: IT NEEDS PAINTING

The house sends a message to the homeowner asking for approval to buy paint and washing

BEGINNING OF THE PROCESS: BUYING THE WASHING POWDER The washing machine includes washing powder in the house’s grocery shopping list.

The homeowner approves the painting and the washing powder bought.

The house sends specifications for painting, quantity and a list of orders to the supplier.

The supplier is informed about the paint and the washing powder. It checks with the house if other items are needed. It sends to the house a list with local partners for the painting.

Supplier does not have the washing powder in stock. Manuacturer offers to send it directly to the client using a drone.

The house warns the car that going to the supplier will no longer be necessary.

Source: Gartner.

The new technologies impact business models and people’s lives. Services rather than products, on the one hand, smart machines destroying and creating new jobs, gathering data and information, on the other hand. In an article talking about business and technological trends for this decade, McKinsey (Bughin, J. et al, 2013) includes other forces to those already mentioned by Gartner. Among them, stand out a tendency for everything (vehicles, real estate, toys, human resources, etc.), not just IT products (infrastructure, software and a development platform), to be treated as a service. This is certainly a significant change to the way that, over the centuries, people have been interacting with tangible goods, favoring the purchase, ownership, rights and duties inherent therein. Detachment from property creates spaces so people can focus on what really matters, ultimately: the enjoyment of goods. It also creates spaces for a society based on service contracts. The service provider will be responsible for ensuring that the asset under negotiation is in appropriate conditions of use, being in charge of the tasks of revision, maintenance, replacement and proper disposal, when applicable. The service contractor will be in the relatively comfortable position of paying an appropriate amount for the use of the asset for a certain time. Within the new paradigm, new questions come up concerning the way the goods will be produced and maintained, since the costs involved in the effort required to produce must be distributed among a series of potential consumers. The increased uncertainty regarding the new business tends, therefore, to impact the logic of production, storage and movement of products.

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Another trend concerns the importance that some markets, so far little explored, will have in future businesses. Markets in developing countries, in general, and mass markets, in particular, start playing a relevant role in the new context. However, and this is a difference from the traditional mass market, even in this market profile, consumers now want to feel unique and special, and cooperate more actively with the processes (prosumers). The solutions focused on the mass market must be sustainable, easy to use and low cost. They must also be strongly oriented to the needs of each customer, considering their contexts and specific use cases. That is, the new solutions are the result of a sound knowledge of consumer needs and desires, being able to incorporate the moment it experiences. They do not consider only preset standardized situations, but are context-sensitive and able to learn from it. The variability and uncertainties are gaining ground. The advent of the smart machines is also an impacting force. In many cases, they will be used to support tasks performed by workers, but, in others, they will replace them, which will lead to a significant destruction of jobs, on the one hand, and to the creation of new jobs on the other. Not only employment in routine activities will be affected (and this is a something new compared to the past technological revolutions), but also the higher-value activities, currently performed by knowledge professionals. If robots will quickly and efficiently do the work which is now done by humans, what are the tasks in which they will engage in the future? Will there be jobs for all in the manufacture of smart machinery or only a minority will be involved in the manufacturing process? Who will have access to data, information and to all the knowledge accumulated by the smart machines while performing their daily activities? In future societies, how will the work and the wealth be distributed among companies and countries?

CHANGES IN STRUCTURE AND DYNAMICS OF THE IT SECTOR Technological changes and new business models deeply affect the IT sector. They beckon with opportunities, but pose risks for companies as they operate today. They impact the development process of software and IT services, the structuring of steps of the product/service life cycle, the premises for the design of solutions, the activities of basic and applied research, among others, reorganizing the structure and dynamics of the sector. These points will be discussed below.

Development process of software and IT services Bimodal development models become common. Bimodal software development models tend to become increasingly common. The classical development way begins being practiced in parallel to the adoption of new methodologies (agile methods, kanban & devops). Focused on IT, the traditional method of software development brings confidence and security, being oriented to a previously conceived plan that considers long-term delivery cycles and infrequent contacts with the customer. The new methods, in contrast, provide speed and agility. Having the business in focus, they take into account the customer experience and its rapid and continuous feedback, maintaining short delivery cycles. The traditional way of development clings to record systems and protocols, which increases governance, but limits the capacity for change. Conversely, agile methods reduce governance, but create more opportunities for change, facilitating innovation.

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Although both practices must now be considered by IT-developing companies, there are barriers preventing the use of the same team in both development methods. This is because the abilities and attitudes required from the professional are very distinct.

Steps of the development cycle for software and IT services Shift in emphasis in the phases of the software development life cycle. With the advent of smart machines, there will be a shift in emphasis in the phases of the software development life cycle. Less noble and repetitive parts of the process (coding, testing and revisions) will be the first to be performed by robots. IT professionals will focus on software life cycle activities with a higher added value (design, specification, choice of platform, architecture definition). Activities with higher value will also undergo transformations. The way the processes work today and how people are inserted in them are not a good reference for the design of future processes, which will take into account the context and focus on digital moments, involving people, processes and things. To survey the new requirements, the technical skills of IT professionals will need to be combined with social and human skills, to generate powerful insights into the use cases and the experiences of users. In the future, the use of good algorithms to support decisions to be taken by things will be critical. The new way of designing products seems to consider that markets recognized before as characteristic of business between companies, i.e., from an IT supplier to an IT-contracting company (B2B) are treated as markets between IT supplier and end consumer (B2C). This is because, increasingly, even in B2B markets, the end user will be the one to command the new process drawings. With the advent of the Internet of Things, the trend from B2B to B2C will be replaced by the supremacy of B2C2T markets, with things permeating relations between suppliers and consumers. The advent of C2C (consumer-to-consumer) markets or C2C2T (things involved in relations between consumers) will also be increasingly common. That is, platforms that do not provide any specific product or service, but create and maintain an enabling environment for people to interact, exchange ideas and do business will begin to emerge increasingly. The compensation obtained by the platform ‘owner’ may come from platform maintenance or result from monetizable businesses arising as a result of the existence of the environment. This occurs, for example, with the transformation of data that typically circulate on these platforms in knowledge and insights of high market value.

Unusual business models The traditional use license gives way to software as a service. Micro-services and services made easier by IT gain ground. The way with which software was marketed in the past, based on the traditional use license, will open an increasing amount of spaces for the compensation format based on duration of use (SaaS model - Software as a Service). Along with the new form of marketing, there is also a review of the content to be delivered. The user wants to pay only for what they are interested in consuming. The new reality determines the end of

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comprehensive and complex software suites, like ERP (Enterprise Resource Planning). It brings to the stage the interest in micro-services (including only a few functions of what used to constitute a software module) which may or may not be provided by the same player, and organized and combined according to the specific needs of each customer. In many cases, software will not be marketed as such, but will be used by companies to generate more efficient and smart services to customers, a model that was conventionally called ITES (Information Technology Enabled Services): i.e., provision of services made easier through IT. As examples of ITES, we can mention the execution of forest inventories on demand, the smart spraying of pesticides by a company contracted to do it, etc. IT companies will be able to provide themselves the new services or develop solutions for companies with profiles of service providers. Sustainable business cycle: an also possible source of revenue. Another possible source of revenue for software companies will emerge from the creation of a sustainable business cycle. In the sustainable model, the service provided to a customer can produce data that, associated with more data from other sources of information, generate knowledge and insights able to leverage various high-value businesses. In order to obtain the data they need, IT companies can also choose to provide some free services to their customers as a way to benefit them for their contribution. New skills will be needed. Access and the skills required to handle large volumes of data are key ingredients for the success of future IT companies. Considering a world increasingly populated by smart machines, sensors and actuators, IT suppliers should also include in their luggage of future skills the knowledge in robotics and embedded electronics. It is still not all certain what will become of the embedded software industry. Hardware companies will keep developing software to be used in their machines under their control or will there be room for the emergence and growth of companies specializing in the development of this type of software? In this case, the market will be taken by companies that, traditionally, are already positioned in the software market, by spin-offs of hardware companies or new entrants? Regardless of the business model, one question that arises is who will have access to the data collected by the various sources.

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Changes in the software value chain Traditionally, Brazilian software companies funded domestically have positioned themselves in the applications market, becoming followers of leading companies. In the computing paradigm dominated by mainframes and, after them, by personal computers, the software sector is characterized by the presence of a few large and global companies, platform leaders. Around them, a series of small specialized companies with complementary solutions, targeted to specific areas of expertise and to meet the local markets. Large companies from the hardware sector and pure software companies formed the tandem of pioneers in the market of software for microcomputers. Some of the candidates for market leaders survived because they were successful in mastering the infrastructure and middleware segments of the software value chain, driving out competitors. The consolidation of these companies in these segments triggered two factors. On the one hand, it created strong barriers preventing the entry of other direct competitors. On the other, allowed the emergence of an ecosystem of followers, consisting of small software and IT services companies, to operate as partners of the leaders, offering applications to end customers of different sizes located in various territories and in different areas of application. While the infrastructure and middleware companies offer generic and replicable solutions in different markets, applications companies work with specific solutions for each area of application, customer size and location. While the first prospect new technologies, the latter focus on the market and in the attempt to understand the customer’s processes and needs. For application software companies, proximity with the customer has always been a critical success factor, for insights came from them for improvements in products and services. The local presence of the IT company was also required for customization of solutions, training and technical support (Figure 3.2).

Storage management

Database management

Safety management Management of networks & services, etc.

Platform for integration/SOA Data analysis extraction, etc.

Vertical: health, education, telecommunications, agribusiness, financial automation, commercial, etc.

■■ Companies ■■ Organizations ■■ Government ■■ Individuals

Characteristic: Additional modules Developers: Satellite & core countries Scope: Local, regional or global Market structure: Many (small- and medium-sized) companies and leaders with an end-to-end proposal Specific solutions

Characteristic: Technological platforms Developers: Core countries Scope: Global Market structure: Large (and few) companies Generic solutions, for various markets

Upstream

Horizontal: business management - ERP, HR, CRM, tax systems, accounting systems, etc.

END CUSTOMER

Development platforms APPLICATIONS

Operating systems MIDDLEWARE

INFRASTRUCTURE SOFTWARE

FIGURE 3.2 – SOFTWARE VALUE CHAIN: STRUCTURAL SPECIFICS CONSIDERING THE DIFFERENT LINKS

Middlestream

Downstream

Source: Softex Monitoring Centre.

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There is a series of advantages to position downstream of the software value chain, assuming the position of a follower. The advantages include the reduction of business risks, access to privileged information from leading companies and the possibility to use its strong brand, recognized in the market. However, the position of follower can bring about a series of threats. The barriers preventing the entry of new players in the applications market are lower, which opens the possibility of the company having to compete with a larger number of competitors. Applications lead to lower scaled gains, as they serve a smaller quantity of customers. There is always the risk of discontinuity of the leading platform or that the leader has an interest in entering the market of the followers, focusing vertically, assuming an end-to-end positioning strategy. Thus, among the determinants of success of a follower company is the right choice of the leader and the search for the development of products/additional services that do not confront those already offered, or which may eventually be offered in the short term by the leader. Some leaders are positioned in the various links of the software value chain, offering application, horizontal in general, of the ERP type, for large customers, as these are more likely to properly remunerate them for their development efforts. The new technological forces and business trends lead to changes in the structure and dynamics of the IT sector. The new technological forces and business trends operate in the sense of changing the structure and dynamics of the IT sector, so far predominant. The adoption of software marketing in service mode (SaaS) creates opportunities for business leaders to expand its presence in the applications market, starting to also understand small customers, offering light versions of their solution with lower complexity. That is, with SaaS, the large leaders operating in the applications market manage to implement a bimodal format of marketing, expanding, without harming, their businesses: keeping the traditional license of use for the full version for large customers; selling a light version in SaaS format for small customers. The adoption of a bimodal proposal by the large companies tends to affect the market for small applications companies. For them, who had access, in general, to small-sized customers, it may be more difficult to replace the marketing format of their products, as this change, in principle, entails a change in the marketing model already adopted with the customer, and may cause, at least initially, a break in their usual revenue flow.

Specialization with opportunities for scaled gains The market populated by generic global solutions tends to lose ground to the market made up of very specific global solutions. In the new context, scaled gains are no longer a privilege of generic proposals, with the delivery of specific services possibly gaining a global reach. That is, there are now greater possibilities for scaled gains in the applications market. With remotely delivered services, it is easier to access customers and remote markets. In addition, software fragmentation into small services helps in making them more intuitive and simple to use, eliminating the need for physical proximity. In contrast, reducing the complexity of software usage will require an increase in complexity of development. The software should have more intelligent algorithms, able to anticipate the customer’s profile and their needs.

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Proprietary technological platforms x open platforms; vertical concentration x specialization It is likely that middleware and infrastructure software are increasingly offered by consortia of companies. In the new scenario, the lock-in effects are mitigated. Due to the uncertainties and risks involved with the necessary change of technological paradigm and the urgency to review the portfolio of IT products and services, making them more intelligent and sustainable, it should be expected that middleware and infrastructure software are increasingly offered by consortia of companies. The establishment of strong partnerships expands the actual chances of a given technological proposal to actually be successful. In addition to reducing risks and uncertainties, partnerships allow the dilution of the high costs of R&D among the various participants in the initiative. In this context, open standard platforms, allowing interoperability and exchange of data and information, emerge as an alternative to the traditional proprietary model. The use of open technological platforms combined with the adoption of cloud computing, the Saas marketing model and the strong trend for software fragmentation into micro-services work to allow for easier replacement, for with less costs, risks and uncertainties of a given product or service for the other, which provides more power to the end consumer. Relations between following and leading companies are also modified in the new context. The lock-in effects are mitigated due to the reduction in gravitational force that heavily drew followers and their clients to the orbit of influence of the leader (Figure 3.3).1 FIGURE 3.3 – LOCK-IN EXTENUATORS: POSSIBLE CHANGES TO THE STRUCTURE AND DYNAMICS OF THE SOFTWARE INDUSTRY FROM:

Distribution

Consulting

Application

Infrastructure & middleware LEADING PLATFORM

Application

TO:

Application

Consulting

Distribution Application

Application Support

Application

Support

Consulting

Application

Support Support

Application PLATFORM 1

PLATFORM 2

PLATFORM 3

Source: Softex Monitoring Centre.

1 For more information about the changes in the structure and dynamics of the software and IT services industry that take place with the emergence of the new technologies and business models, see Softex Monitoring Centre, 2015.

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In the new context, there is, therefore, tension between models based on proprietary platforms and models supported by open formats. Another arising tension that causes uncertainties about the future, concerns the trend for vertical concentration as opposed to the search for an ever greater specialization. As a form of resistance and survival in the new scenario, the market leaders may seek to position themselves strategically in the various links of the value chain of hardware and software and IT services, dramatically reducing the opportunities for emergence and strengthening of specialized companies. On the other hand, it is possible to notice that the scenario is still open to the possibility that the solutions of the future will be, increasingly, the intelligent combination of an infinite series of goods and services generated by small specialized companies. The way how, in the future, data and information gathered by the various collection sources will be used by communities, in general, and by IT suppliers, in particular, is a relevant issue, perhaps the most relevant, in the gradient that goes from a strong vertical concentration to a specialization as a rule. This is a battle that is being fought at the international level, in discussion forums on reference models, architectures and protocols, etc., involving the not always coincident interests of the market leaders. Possible models: oligopoly, conglomerate, star network and open network. Box 3.1 shows the open possibilities. On the one hand, the tension between models based on proprietary and open formats. On the other, the chances that, in the coming years, there is a strong vertical concentration or that the scenario is dominated by an intense process of specialization. They are, of course, ideal models. The most likely is that none of the alternatives occurs in its purest way, the reality being a combination of several possible models: oligopoly, conglomerate, star network and open network. At the national level, public policy actions and the building of a regulatory framework for the new IT context will play a leading role in the more or less favorable consolidation to one of the possible scenarios.

Concentrated

BOX 3.1 – IDEAL MODELS, CONSIDERING POSSIBLE ALTERNATIVES, IN THE NEW CONTEXT

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OLIGOPOLIZATION • The winner takes it all: a few leaders dominate the market, working end-to-end. There is no room for new entrants. • Difference from business leaders occurs in the hardware market and/or infrastructure software. • Hardware Developers develop the embedded software on their machines and devices and equipment. • Customers find it difficult to change suppliers. Strong lock-in effects. • The data and information gathered are for the exclusive use of the proprietary company and their customers.

CONGLOMERATE • Major players operating together in hardware development and infrastructure software. Differentiation occurs in the applications market. • There is no room for new entrants. • Customers can change suppliers more easily. Lockin effects mitigated. • The data and information gathered are for the exclusive use of each company participating in the consortium.

Specialized

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

STAR NETWORK • Large companies dominate the hardware market and/or infrastructure software. • Each leader builds their own network, allowing the entry of an unlimited number of companies in the applications market for complementary offerings. • Customers find it difficult to change suppliers. Strong lock-in effects. • The data and information gathered are for the exclusive use of partners and their customers. • Island scenario, of people who do not talk or exchange information with each other. • A lot of companies orbiting around few leaders.

OPEN NETWORK • Major players propose a joint platform. • Dissociation between hardware and software suppliers. • Differentiation occurs in the applications market. • Scenario favorable to the emergence of companies in the applications market. • Customers can easily change suppliers. Lock-in effects mitigated. • Data and information are used by all.

Proprietary

Open

Source: Softex Monitoring Centre.

The subject dealt with in this section will be taken up further, in Section 3.4, having in mind the AgroTIC reality. Prior to that, in Section 3.2 below, a survey of the technologies which are being specifically thought out for the agribusiness sector will be made. In Section 3.3, an analysis of the options of software and IT services that are being offered in the Brazilian market will be conducted.

3.2 NEW TECHNOLOGIES IN AGROTIC Masshurá et al (2014) summarize the potential use of information and communication technologies in agribusiness (AgroTIC), taking, as a starting point, the segmentation of the agribusiness value chain in the processes of pre-production (genetics and seeds), production (planting and harvesting) and post-production (distribution, processing and consumption) (Figure 3.4). For the authors, the agricultural pre-production step will require more and more skills in data mining and mastering of modeling and simulation techniques. Simulation, as a matter of fact, will be a strong ally of AgroTIC, allowing the construction of future scenarios and testing products and services in controlled environments before introducing them in the field. The great volume of data generated by biotechnology and bioinformatics, involving genomics, will require the use of high-performance computers. The production step is a scene that is proper to the adoption of precision agriculture and robotics. Planting and harvesting activities will undergo an intensive process of automation, with significant adoption of remote sensing and the use of geographic information systems (SIG). In post-production, the social technologies will gain prominence; cloud computing, to ensure the context of mobility and scalability of the computational infrastructure; and advanced analysis/big data, for storage and processing of large volumes of data collected in the field and in other sources. In this case, the use of data mining techniques, geo-processing, communications and optimization and the search for smart solutions for decision support prevail.

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Despite being specifically mentioned in the post-production process, cloud computing, advanced analysis/big data and knowledge management are technologies increasingly more present throughout the value chain of AgroTIC. In pre-production, for example, advanced techniques of sequencing of genomes, aiming at selecting the genes capable of conferring the desired characteristics to the target species will generate huge amounts of data. Similarly, in the production process, the devices and equipment used in precision agriculture will collect significant amounts of data and information, requiring proper capacity for its storage, handling and distribution. Indeed, perhaps the greatest novelty introduced by the new context of technologies is precisely obtaining a lot of data, from various sources, which can be crossed among themselves. And the great wealth brought about by the increase in this amount of data is precisely the possibility to use them for prevention and prediction, improved productivity, reduced costs and welfare of the population. FIGURE 3.4 – POTENTIAL USE OF ICTS IN THE AGRIBUSINESS VALUE CHAIN Pre-production

Genectics

Seeds

Data mining High-performance computing Modeling and simulation

Production

Planting

Harvest

Remote sensing GIS Automation

Pos-production

Distribution

Processing

Consumption

Data mining Geo-processing Otimization Decision support

Social technologies Biotechnology

Precision agriculture

Cloud

Bioinformatics

Robotics

Mobility Big data/analytics

Source: Masshurá et al. (2014).

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This section, from a summary of technologies mentioned in Masshurá et al. (2014), introduces the state-of-art of AgroTICs. Opportunities and challenges are also explored.

Bioinformatics is an interdisciplinary field that uses computational and mathematical techniques to analyze and manage biological data and information. Software is used, for example, to identify genes, to predict the three-dimensional configuration of proteins, to identify inhibitors of enzymes, to organize and list biological information, to simulate cells, to group homologous proteins, to assemble phylogenetic trees, to compare microbial communities for the construction of genomic libraries, to analyze gene expression experiments, etc. (Wikipedia).

The use of Bioinformatics in agribusiness aims at the genetic, plant and animal improvement. A significant amount of genomic data from plants, animals and microorganisms is already available. The trend is the generation of an increasing amount of data (Giachetto, P. et al, 2014).

Development of new data integration tools, and solutions for storage, processing and handling of large volumes of data (Giachetto, P. et al, 2014).

Projects, groups and research lines

State-ofthe-art

Biotechnology is the series of techniques that allows the cultivation of microorganisms to produce medicines and strawberry cells (for example) to obtain commercial seedlings. It is also a process that allows the treatment of sanitary sewage by microorganisms in septic tanks. It covers different areas of knowledge, including basic sciences (Molecular Biology, Microbiology, Cell Biology, Genetics, Genomics, Embryology, etc.), applied sciences (immunological, chemical and biochemical techniques) and other technologies (Computing, Robotics and Process Control) (cited in http://www.ort.org.br/biotecnologia/o- que-ebiotecnologia/).

Challenge/ opportunity

Description

BIOINFORMATICS/BIOTECHNOLOGY APPLIED TO AGRIBUSINESS

Unicamp Bioinformatics Laboratory (LaCTAD) - Operates in areas of Cell Biology, Genomics, Proteomics and Bioinformatics offering high quality services to the scientific community and private companies. Its mission is to provide services, ultra-modern technologies and technical advice in the areas of Cell Biology, Genomics, Proteomics and Bioinformatics, effectively contributing to increase the impact of the Brazilian scientific research. Bioinformatics Multi-user Laboratory (LMB) - Due to the great demand for computing power and multidisciplinary skills to handle large volumes of data, algorithms and analysis tools, Embrapa opened the LMB, whose mission is to enable bioinformatics solutions for research, development and innovation projects in a collaborative environment, incorporating and providing for the scientific community new technologies for storage, processing and analyzes of large volumes of data. The technical capacity building, through courses and training in tools used for data analysis, is also part of the operations at LMB. Genomics Research Applied to Climate Changes (Umip GenClima) – to identify and validate new genes with biotechnological value and develop genetic constructions with scientific and commercial values containing new genes which may be transferred through genetic transformations for commercial varieties of plants developed by Embrapa.

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Software available

BIOINFORMATICS/BIOTECHNOLOGY APPLIED TO AGRIBUSINESS (continuation) • Potion, from Embrapa Informática Agropecuária – software that detects groups of homologous genes on evidence of positive selection in genomic scale, having been ideally designed to run on servers with multiple processors, though they also work in desktops. It is a modular, easily scalable software that uses various programs which are the state-of-the-art in their respective fields, such as OrthoMCL to detect homologous groups, Muscle to align the groups of homologous proteins, Phylip to build phylogenetic trees and PAML to detect positive selection. The final produced program has approximately 1,500 code lines and uses several sophisticated bioinformatics modules previously developed for Perl (Bioperl). The user may control the behavior of all third-party software through global parameters set at the beginning of the pipeline run. The tool was developed through a project funded by the CNPq (the Brazilian National Council for Scientific and Technological Development) and, considering its specificity and potential to contribute to the progress of knowledge and bioinformatics research, it was made available for free on the GoogleCode platform as a free open source software. • Scylla EST, software for managing ESTs (Expressed Sequence Tags) sequencing projects from Scylla Bioinformatics (www.scylla.com.br). • Startup Cell Seq (www.cellseqsolutions.com.br) provides solutions for more assertive analyzes in order to meet the growing demand for in vitro tests to develop new products aiming at replacing or reducing animal testing. It also offers solutions in bioinformatics, which generate relevant information from the massive amount of data generated through next generation sequencing.

Description

PLANT PHENOTYPING Plant phenotyping is a new field of application for the computational vision in agriculture. The data collection techniques used are not destructive. Thus, it is possible to collect data during the growth and development of the plant. Plant characteristics that have never been measured before or only in specific situations begin to be measured more frequently. Dynamic processes can now be described along time and space. In addition to the phenotyping technologies used in the laboratory or in controlled environments, most characteristics of agronomic importance also need to be evaluated in the field. For this purpose, various types of sensors have been developed and coupled to manual equipment, tractors and other machines, and also to towers, blimps or drones (Santos, T. et al, 2014)

State-of-the-art

Three-dimensional models may work as a concise and versatile temporal representation of plant conditions, allowing different quantitative measures to be computed. The behavior of the specimen

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over time can be evaluated by comparing the various three-dimensional models constructed over time (Santos, T. et al, 2014). Agricultural researches usually require field experiments for verification on actual production conditions. Such environments are more complex for phenotyping on a large scale and to 3D reconstruction of plants due to the variability in lighting conditions, movement caused by the wind, etc. Today, costs limit the use of phenotyping technologies to biotechnology industries and seeds and to public projects. Continued progresses in the development of sensors, cameras, imaging methodologies, automation, among others, will provide reduced costs and expansion of its application.

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

PLANT PHENOTYPING (continuation)

Challenge/opportunity

Even with the use of the new technologies, many of the equipment available for large-scale phenotyping still require operator handling. To minimize the individual bias obtained by manual measurements and to ensure scale, accuracy and precision in measurements, mechanization and automation of some processes emerge as an alternative (Santos, T. et al, 2014). Generic platforms and modular and flexible solutions that enable the simultaneous evaluation of the phenotype of multiple species and able to accommodate different experimental needs are not yet available. The phenotypic analysis of the plants will produce an ever increasing amount of three-dimensional data, which requires methodologies capable of analyzing these data, automatically identifying the structures of interest, performing the measurements required to characterize the phenotype and detailing the behavior of those structures over time. New methods in computational vision and machine learning are required to allow the automatic recognition of the same structures over time, for example, to register the development of a leaf throughout an experiment. Merging of data from multiple sensors. The idea is to obtain the most complete snapshot possible of the study of the plant at the time of measurement.

Software available

Projects, groups and research lines

Advancements in modeling and simulation of plant growth. Applications such as structural-functional analyzes in plants, development of plant growth models, phenotypic analyzes for animal and plant genome and augmented reality for instrumentation and control involve the automatic construction of threedimensional models from digital images, so that they can be produced on a large scale.

European Network for Plant Phenotyping - seeks to unite efforts to standardize the phenotyping methodologies used, develop new sensors, instruments and structures to access, manage and analyze the technological information generated.

Phenotyping platforms in development for controlled environments (automated and semi-automated configurations): Phenopsis, Growscreen and TraitMill. Solutions provided for controlled environments: LemnaTec, GmbH and Qubit Systems help automate various stages of the cultivation and phenotyping process as substrate preparation, pot filling, planting, fertilization, irrigation, collection and analysis of phenotypic data. WPS (http://www.wps.eu) – Dutch company which is a partner, in Brazil, of Flórida Estufas Agrícolas (Florida Agricultural Greenhouses). Offers automated or semi-automated systems for classification/evaluation of plants (seedlings, vegetables and flowers) in greenhouses (or wholesalers) through their observation through 3D imaging obtained through a camera attached to a treadmill.

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The process to obtain new drugs has changed over the years. They were the result of random testing in animal cells, plants or their models. The method is becoming increasingly ineffective due to the reduced probability of identifying a new drug. Today, the search for micro molecules is done with computational resources without spending materials and in an extremely short period (Jardine, J. et al, 2014). Preparation of an inventory with new agrochemicals and pharmaceuticals for agribusiness in the Southern Cone countries (Jardine, J. et al, 2014).

Projects, groups and research lines

State-ofthe-art

Branch of biotechnology that seeks to offer, based on Computer Science tools, Applied Mathematics and Statistics, a transdisciplinary perception of aspects related to sequences of nucleotides and amino acids, dynamic structure of proteins and protein-protein interaction, DNA protein and protein-linker. Among the areas covered, there is emphasis for planning and design of new drugs, pharmaceuticals and agrochemicals or agricultural pesticides in a laboratory (Jardine, J. et al, 2014).

Challenge/ opportunity

Description

MOLECULAR COMPUTATIONAL BIOLOGY

Functional annotation for the vast amount of data from sequences and structures and functions of proteins, particularly enzymes, generated by high-performance, large-scale technologies.

Laboratory of the Group for Computational Biology Research at Embrapa Informática Agropecuária. The Group performs research projects in structural bioinformatics with innovative solutions in computational design of pharmaceuticals.

Software available

Sting BD - database with physical, chemical, physico-chemical, structural and biological descriptors on protein structures.

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Blue Star Sting - software suite with visualization tools and structural analysis of proteins. These programs (modules) are concentrated in a single package aimed at providing a tool for studies of macromolecules, their structures and structure-function ratio. Programs PyMol and Accelerys Discovery Studio are used along with Sting for viewing and generation of molecular imaging and analysis of potential absorption of new pharmaceuticals. Molegro - platform for planning and design of new pharmaceuticals/drugs/agrochemicals which also has a strong component in the line of structural data mining and is used in the laboratory at Embrapa for virtual screening and docking of molecules with high precision.

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

Description

The growing concern with the increase in world population, with the degradation of natural resources, climate change and sustainability of agriculture has required efforts to develop more sustainable agricultural practices, including from the better understanding of the relationship between agriculture and the weather. Therefore, government institutions related to agriculture and the environment have been seeking to develop agro-meteorological information tools to help with planning and in the decision-making process in agricultural production, seeking greater productivity and resilience of production systems and lower environmental impact (Monteiro, J. et al, 2014).

State-of-the-art

The agro-meteorological information can be split into three levels (Monteiro, J. et al, 2014): • The first level, with pure meteorological data or meteorological data resulting from simple calculations, such as water balance. • The second level, when they are produced from meteorological data and parameters specific to the culture, indicating the conditions or crop response to the weather conditions observed. • The third level, when they indicate, in addition to the conditions or crop response, the corresponding management action (Sentelhas & Monteiro, 2009, cited in Monteiro, J. et al, 2014). Currently, Brazil has various systems of agro-meteorological information in operation, providing basically the first level of information and some from the second level. Among the agro-meteorological information employed in the agricultural planning phase, the agroclimatic zoning is the most widespread used in Brazil, consisting in determining the ability of the climate in the regions of a country, state or municipality, considering the agro-climatic requirements of crops and the weather information of the location of interest. As the soil is a different component of the physical environment required in agriculture, the edaphic aspects may be considered along with the climate aspects. The agroclimatic zoning may be employed for the delimitation of areas suitable, marginal or unsuitable for planting, and also to establish the best moment for sowing, based on various probabilistic information (Monteiro, J. et al, 2014).

Challenge/opportunity

One of the common characteristics of agro-meteorological information systems is that their primary vehicle for distribution of information is the Internet. In Brazil, the Internet is still very limited in availability to the general public, both by the lack of coverage in the most remote locations of the large urban centers and by the difficulty of access by the lower classes. Lack of accurate data at high spatial resolution, particularly with regard to agricultural soils. It is therefore necessary to form a database of soils with sufficient sample density to allow the refinement of spatialization methods and prepare situational maps in a more detailed scale, or also to allow more specific and timely consultations with less uncertainty. Specific tools which consider the needs of each local culture and conditions, helping farmers more effectively in their decision-making processes.

Software available

AGROMETEOROLOGY AND CLIMATE CHANGE

Inmet’s Sisdagro: comprises various aspects of the effects of the weather and climate on agriculture, helping to predict the harvest, to define the best times for planting, to indicate the handling conditions of the soil, for irrigation and phytosanitary control. Embrapa’s Agritempo: enables the generation of maps considering the following variables: conditions for phytosanitary treatment, prediction of frost, need for irrigation, conditions for soil handling, water availability in the soil, weekly rainfall build-up, the need for replacement by rainwater, maximum, minimum and average temperatures, location of weather stations. The Agritempo system manages data and information from a network of more than 1,400 weather stations belonging to various partnering institutions. Embrapa’s Simulator of Agricultural Scenarios (SCenAgri): used exclusively for research activities, uses the estimated data of models of future climate projections, enabling the assessment of the impact of climate changes on agriculture.

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Description

Remote sensing will be increasingly more used for generating land surface images and obtaining information about objects. Digital cameras installed in aircrafts and orbital sensors coupled to satellites extend their capacity (for example, sensors now have a filter and a chip sensitive to infrared radiation, which is important for vegetation studies; improvements in spatial resolution allow greater details of objects on the surface). In addition to the advancements in image collection, it is also possible to notice improvements in the capacity of analysis and visualization of data obtained.

Challenge/ opportunity

Nowadays there is an unprecedented range of geospatial data produced by different techniques and for the most distinct purposes. The set of techniques (technologies) for collection, processing and analyzes of spatial data includes surveying, photogrammetry, cartography, remote sensing, satellite positioning, geostatistics, geographical databases, webmapping and GIS (geographic information system).

State-of-the-art

ICTs APPLIED TO GEOSPATIAL DATA

One of the great challenges of space-temporal models is to overcome the static view of maps, including an approach based on location and dynamics of human actions (Esquerdo, J. et al, 2014). The evolution of geotechnologies, the speed in the generation of geospatial data and the massive amount of geoinformation produced also pose challenges with regard to the treatment, organization and availability of data in the new big data context. WebGISAmazônia Legal – The harmonious implementation of policies of territorial management, land-use and land occupation is a need in Brazil, particularly in the area of Amazônia Legal, focus of

Projects, groups and research lines

broad concerns with the environment, geopolitics and country development. The Ecological-Economic Zoning (EEZ), a territorial planning instrument, aims to enable sustainability, combining social and economic development with environmental protection. In addition to establishing minimum parameters to standardize and integrate the EEZs of states and reconcile legends and guidelines for usage and occupation in the area, the project intends to make an infrastructure of spatial data available and to develop the WebGIS Amazônia Legal, an online tool to provide the content generated with the acquisition, analysis and unification of project information. The tool also offer basis for decision-making, especially in matters related to environmental management. The Federal Government has been implementing measures to integrate the geospatial data produced by various national institutions. The main one is the Spatial Data Infrastructure (Inde), established in 2008 with the purpose of cataloging, integrating and harmonizing the geospatial data existing in the institutions of the Brazilian government, producers and sponsors of such data, so they can be easily accessible, explored and located over the Internet, for the most distinct usages. Natdata – Embrapa’s Platform of Information on Natural Resources of Brazilian Biomes. Comprises data on climate, soils and biodiversity. Considering the results already obtained, it is possible to notice the importance of a platform that integrates the different types of data in a single location. MODIS Database – Satellite imaging has been an important source of information for studies of ecosystems. Policies encouraging data sharing, combined with the development of online distribution systems, have facilitated public access to satellite images, enabling the development of studies in the most varied subjects. One example is the images of the Modis sensor, the main instrument on board orbital platforms Terra and Aqua, administered by NASA. In order to facilitate access to these products in Brazil, Embrapa Informática Agropecuária began the development of the MODIS Product Bank in the Brazilian State Base, in order to store and make available to the user ready-made images to be used in state cutouts, without the need for any additional processing.

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ICTs APPLIED TO GEOSPATIAL DATA (continuation) Interactive System of Support to Environmental Licensing (Sisla) – in most Brazilian states, analyzes of environmental licensing processes are performed in an analog form, which requires considerable time until its completion. A user who has geo-referential information on their project may obtain the spatial analysis of its surroundings in less than two minutes.

Software available

TerraClass – System of Monitoring Usage and Land Coverage in Deforested Areas of Amazônia Legal – In recent decades, the advances noticed in ICT made it possible to face the challenge of developing and implementing a system to monitor the dynamic of use and the coverage a vast area with an extremely complex access such as the Amazon. The adoption of geotechnologies related to the acquisition, processing and availability of geographic data has enabled the integration of different methods of data processing from orbital remote sensors for systematic generation of maps on the use and coverage of land across the area and its full publication through the Internet. Several companies provide services of geospatial data collection. Some are listed below: Allcomp Geotecnologia e Agricultura (www.allcompgps.com.br) provides equipment for areas of topography, geodesy, cartography, city construction and agriculture. They represent the best brands in the market, offering services of technical assistance and equipment leasing. They have moisture meters and a system that prevents failures and overlapping of applications. Engemap (www.grupoengemap.com.br) offers aerial survey services using their own aircrafts and software of foreign companies for acquisition and post-processing of digital images. They are the distributors of ESRI and Trimble brands and originated, through a spin-off to three other companies: Cadmap, Sensormap and Satmap. Companies Imagem (www.img.com.br) and Geojá (www.geoja.com.br) also offer solutions involving geoprocessing of satellite images and/or aerial photographs. Startup Agropixel (agropixel.com.br) offers their platform of geographic intelligence and data management for small- and medium-sized farmers. A large amount of data is collected from multiple sources and converted into geo-referenced performance indicators, arranged in dashboards and monitored by anomaly warnings. Startup DronENG - Drones e Engenharia (www.droneng.com.br) is specialized in preparing smart digital maps obtained through aerial mapping using drones. It performs strategic management of land through smart digital maps.

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Description

ICTs IN THE ZOO-PHYTOSANITARY SECURITY OF PRODUCTIVE CHAINS The circulation of increasingly larger volumes of food requires measures to ensure that health security is implemented quickly, efficiently and inexpensively. ICTs have been used to increase the level of automation and, consequently, the speed of the phytosanitary control process; predicting health problems and identifying potentially vulnerable areas (Barbedo, J. et al, 2014).). The history of application of ICTs in zoo-phytosanitary problems is very recent. Most of the time, the monitoring of these health conditions is made visually, with visits of specialists to the field. This strategy has its disadvantages: experts are not always available for this monitoring as often as needed, especially in remote locations and due to the associated cost being prohibitive for small producers. Even if there is availability of workforce and resources, such a thorough monitoring may be difficult to make, especially in large properties. As a result, there are efforts to develop computational tools to help fight health problems, detecting, quantifying and classifying diseases in agriculture (Barbedo, J. et al, 2014).

State-of-the-art

The available tools can be classified in automatic and semi-automatic systems. • Automatic systems: perform all operations automatically, based on digital images, with little or no user involvement. The benefits are obvious: the user does not need to have specific knowledge of the problem; computers do not get tired and therefore can make a large number of evaluations or permanent monitoring; computers are not subject to optical illusions that affect human evaluators; and, in general, the operating cost is low. The main disadvantage is related to the dependence of the quality of the digital image database used, since the algorithm will only be able to handle the situations for which it was trained. Detecting, measuring and classifying each bring different challenges of varying degrees of complexity. Although diseases detection is of great importance, the literature is not very rich with proposals specifically oriented for this purpose. The measurement of disease severity is very important in the context of sanitary control. The automation of the symptom severity measurement has received great attention in recent years. The classification of diseases is, in general, more complex, since, in addition to detecting the disease, the algorithm must attempt to identify it. The problem becomes more difficult as a larger number of diseases are considered. • Semi-automatic systems: in addition to having a computer part that performs certain operations that lead to the diagnosis, they also depend on a human participation for its correct operation. These systems can be classified into two types: manual correction of results, where visible faults or errors can be corrected by the user; and expert systems. These are originated in the universe of cultures and diseases for which the system has been trained and, through questions, the possibilities of answer are successively refined. Typically, expert systems used to diagnose diseases in plants need to consider an extensive range of problems, making it necessary to create a comprehensive set of rules, which should relate coherently to result in a good diagnosis.

Desafio/ oportunidade

There are also the disease warning systems in plants. Warnings help producers determine the need and the moment to implement disease control techniques. In order to succeed, a warning system must be adopted and implemented by producers, and there must be a perception that it is possible to obtain specific and tangible benefits with its use. Attributes that ensure success include: reliability, simplicity of implementation, importance of the disease, usefulness of the warning, availability to producers, applicability to various diseases/pests and cost-effectiveness.

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Automatic systems for detection, measurement or identification of diseases are highly dependent on the databases used. Currently, the lack of comprehensive databases is the main problem, since the imaging processing techniques and computational intelligence are mature enough to allow the development of truly effective methods. In the processes of detection, quantification and classification of diseases, the plants have received more attention than the animals. Especially for animals, much remains to be done.

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

Projects, groups and research lines

ICTs IN THE ZOO-PHYTOSANITARY SECURITY OF PRODUCTIVE CHAINS (continuation) Embrapa’s Digipathos project aims to contribute to the construction of more complete databases, to allow the development of good automatic systems, with two actions: creation of a database containing images of disease of at least twenty species with commercial value in Brazil; development of a method to identify plant diseases. The method must be based on machine learning techniques, pattern recognition, mathematical morphology and expert knowledge (Barbedo, J. et al, 2014). Sistema Diagnose Virtual (Virtual Diagnose System - Embrapa): online diagnosis of plant diseases, supporting farmers, agronomists and agricultural technicians in their decisions on the handling of diseases. The system is capable of providing diagnosis for crops of rice, beans, corn, soybeans, tomato and wheat. Sistema Uzum (Uzum System - Embrapa): serves as a guide to assist in identifying potential agents of symptoms in vines, providing useful information for a rapid initial diagnosis. The system has a record with 37 diseases, pests and physiological disorders. SisAlert (Embrapa): online warning system to predict plant diseases based on modular and generic simulation models to predict the development of diseases from meteorological data obtained from automatic weather stations and short-term weather forecasts.

Software available

SafCafé (Embrapa) – warning system for coffee leaf rust. Gotas (Droplets - Embrapa) – Available for desktops and also in the mobile version, a software that helps calibrate the spray deposition of phytosanitary products in order to make this process more efficient and avoid waste. The process of production of droplets or spray has, in agriculture, a key role in the production of any plant culture. Its application consists of placing the phytosanitary product which is inside the droplet (syrup), on the surface of the desired plant (target). Aiming to protect the crop against the damage that can be caused by external agents such as pests and weeds, the class of products most widely used in applications is pesticides or agrochemicals. The application is carried out through spray nozzles present on the agricultural implement, with the analysis of droplets produced by these being one of the primary ways of quantifying the efficiency of the application. The distribution, size and spectrum of the droplets, for example, are factors commonly used to evaluate a spraying process. Software Gotas was developed within this context, aiming to help farmers so that they can properly calibrate the spray nozzles and obtain appropriate parameters of deposition of pesticides on desired targets. Agrotec Tecnologia Agrícola e Industrial (www.agrotec.etc.br) keeps reselling equipment for agricultural and terrestrial aircrafts, including software for diagnosis of pre- and post-application of pesticides. The company offers a proprietary software for analysis of droplet deposition resulting from spraying through agricultural aviation. In the traceability/food safety area, the companies that stand out are Check Plant (www.checkplant.com.br), with a solution that allows the consumer to online track the origin of the food, and companies Pari Passu (www.paripassu.com.br), Agromanager (www.agromanager.com.br) and Ética TI (www.etica-ti.com.br).

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DECISION SUPPORT SYSTEMS AND INFORMATION SYSTEMS FOR INTEGRAL MANAGEMENT OF THE AGRICULTURAL PROPERTY

Descrição

Figure 3.5 exemplifies the cycle of handling operations in the field, considering the application of precision agriculture. Geo-referenced information on soil attributes are collected, conveyed and analyzed so that the productive capacities of the land areas are established. From the analysis, machinery and equipment receive instructions for the automated application of lime and fertilizers at a variable rate. The operation of automated sowing/or planting (seedlings) occurs then, using plants which are adequate to the different productive capacities of the soil. Then there are the crop handling operations. Remote sensors (available in satellites, aircrafts or drones) help determine biotic stresses (pathogens, insects and weeds) and abiotic stresses (nutritional and water deficiencies). The information received from the sensors is stored and conveyed to a central, in an interchange-standard language (e.g.: AgroXML). Once there, they will be processed and analyzed by a specific system of decision (SSD), which will send instructions in accordance with standard Isobus 6 for machines equipped with automated steering system and other equipment to carry out geo-referenced operations of application of inputs (water, fertilizers, pesticides, biological control agents, etc.) at varied rates. The cycle is restarted after harvesting, with the use of productivity and/or quality sensors, which data are sent to the processing central for obtaining maps (Luchiari Jr., A. et al, 2014). FIGURE 3.5 – CYCLE OF HANDLING OPERATIONS IN THE FIELD: ILLUSTRATION OF THE STAGES OF THE PRODUCTION SYSTEM AND THE PROCESSES OCCURRING IN THEM

Geo-referenced collection of attributes of the terrain Application of lime and fertilizers at variable rates Geo-referenced harvest

Application of inputs at variable rates

Source: Luchiari Jr., A. et al, 2014.

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Collection, transmission, processing and analysis of data for decision-making and automation of agricultural operations Sowing and planting at variable rates Monitoring of biotic and abiotic stresses of crops

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

DECISION SUPPORT SYSTEMS AND INFORMATION SYSTEMS FOR INTEGRAL MANAGEMENT OF THE AGRICULTURAL PROPERTY (continuation)

State-of-the-art

Precision agriculture and automation technologies for annual, perennial and semi-perennial crops, covering from operations of soil preparation to harvesting and fleet control, are already available on the market. These include, among others (Luchiari Jr., A. et al, 2014): 1) Active and passive sensors, able to determine, in real time, the nitrogen needs required in different cultures, determining the presence or absence of weeds, insects and diseases. The geo-referenced location of the monitored points and the doses applied are sent through a cell phone or Wi-Fi to a station of storage, control and analysis of information, for recommendations. 2) Autopilots to increase efficiency of use of inputs (application of inputs at variable rates), making applications in predetermined locations. 3) Software and hardware for operational handling of the fleet of equipment in real time, remotely diagnosing conditions for maintenance, use and performance of machines and equipment for activities of soil preparation, planting, spraying, harvest, transport and other operations. 4) Software for data processing and construction of crop maps. Some companies now offer solutions that use cloud computing infrastructure, where agricultural equipment is connected wirelessly and information is made available in real time and accessible through Internet browsers or through applications installed on mobile devices.

Projects, groups and research lines

Challenge/opportunity

Standards for integration and interoperability of data in precision agriculture (Luchiari Jr., A. et al, 2014). Policy of property and access to AgroTIC data. Exploration of data collected in the field in order to support the decisions that will shape the direction of the crop, and their integration with information from different sources so that this can be explored in a systemic way. Systems capable of managing this task are commonly called Farm Management Information Systems (FMIS). Due to the amount of data and information obtained, data processing and analysis should be executed in high computational performance infrastructure, such as cloud computing, grid, parallel processing, among others. Development of information systems that expand management beyond property, covering the entire agribusiness value chain. Embrapa’s Precision Agriculture Network (PA Network): aims at maintaining memory, preservation, retrieval and exchange of data produced by Embrapa’s pilot units. Therefore, they developed a repository of information resources using the metadata profile (Brazilian Geospatial Metadata Profile, version approved in 2009 by the Planning Committee for a National Infrastructure of Spatial Data, Concar) to catalog geospatial data, with architecture enabling integration and interoperability between applications. The repository of the AP Network and its results allowed the establishment of appropriate standards to operate, store, retrieve, exchange and interoperate data and information obtained in the pilot units, quantitatively and qualitatively. The experience could be expanded to handling of agricultural properties in the future.

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DECISION SUPPORT SYSTEMS AND INFORMATION SYSTEMS FOR INTEGRAL MANAGEMENT OF THE AGRICULTURAL PROPERTY (continuation) Applications for mobile devices (iPhone, iPad and/or Android): Weedalert, Aphid Speed Scout, Pestbook, Soybeans Diseases, IPM Toolkit, Crop Nutriens Def, Fertilizer Removal, TankMix, Corn N Rate Calculator, N Price Calculator, Extreme Beans, Corn Yield Calculator, Planting Pop Calculator, Irrigation Cale App and CE Budgets (source: Ciampitti, cited in Luchiari Jr., A. et al, 2014). These apps are available for free or at low cost. Gotas (Droplets - Embrapa) – tool for calibration of pesticide deposition in precision agriculture.

Software available

AgroClimate (Embrapa) – decision support system to be used in handling practices in specific sites. It reports on climate risks and helps identifying the best handling practices to be used in agricultural production to mitigate or reduce specific risks. Embrapa`s WebAgritec: computer system for online access and use aimed at assisting the decisionmaking process of professionals linked to the agricultural sector. The system seeks to assist the producer in various stages of planting of crop, working from seed selection to harvesting. It consists of an online system of planning, prediction and monitoring of the agricultural production that gathers information on the following crops: rice, beans, corn, soybeans and wheat. The WebAgritec user can obtain various information: most appropriate time to plant; most appropriate cultivars for their purposes; indications of liming and fertilization for each crop; predictions and trends on weather conditions during the harvest; nutritional deficiencies and diseases; monitoring of the crop with productivity estimate; and monitoring of the crop through the agenda of the property. Company Irriger Connect (www.irriger.com.br) offers an irrigation management system that takes daily decisions on the need for irrigation. In order to recommend the use of lime and fertilizer at variable rates, the system monitors the soil through laboratory analysis, weather conditions (rainfall, temperature, relative humidity, wind speed and solar radiation), using data from weather stations, and the evolution of the crop over time, through satellite imagery. Startup Treevia Forest Technologies (www.treevia.com.br) uses new technologies to offer solutions for forest monitoring and measurement. Includes agility in data collection, real-time information, control of environmental variables and production control. A list of several companies with products and services for precision agriculture is available in http://www. agriculturadeprecisao.org/br/guiaap/. These and other AgroTIC companies are mentioned in Appendix 3.A1.

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Description

AUTOMATION OF MACHINERY AND AGRICULTURAL IMPLEMENTS: EMBEDDED ELECTRONIC, ROBOTICS AND INFORMATION MANAGEMENT SYSTEM Embedded electronics, i.e., the set of electronic systems with processors (hardware) and dedicated programs (software or firmware) for data collecting, processing, storing and communication is increasingly being used in agriculture. Standardization is essential to enable embedded electronics in agricultural machinery and implements to the extent that it avoids the duplication of installation, eliminates obsolescence by compatibility, enables interchangeability, reduces maintenance costs, releases the farmer from exclusive suppliers of commercial systems and may allow for the simplification of integration of information with computer systems external to the machines. The standard currently used is Isobus (ISO 11783) (Sousa, R. et al, 2014). In the state-of-the-art for automation of agricultural machinery, the Technology of Application at Variable Rates (VRT), the On-The-Go systems and Autopilot stand out (Sousa, R. et al, 2014). • VRT Technologies: allow the controlled application of inputs (fertilization and spraying) or planting control (spacing and number of seeds) according to a recommendation map. Agricultural machines come with an automatic speed control and a GNSS receiver for recognition of geographic coordinates. They also come with a computer support system for study and generation of recommendation maps. • On-The-Go Technologies: perform sensing, processing (decision making) and actuation during the machine movement. Systems based on these technologies do not require geo-referencing for navigation. They include a dynamic control unit that determines the application through a real-time analysis of the measures of a sensor of soil or culture for each location within the covered field. In general, they require a precise navigation control, reduction in the system’s response time and a computer system to generate immediate recommendation to the application system (implement).

State-of-the-art

• Autopilots: enable autonomous navigation and a more precise navigation in the field. They are normally applied for feasibility of night work, planting, precise application of inputs and support for the aforementioned techniques (VRT & On-The-Go). Several sensors for application of On-The-Go inputs have been surveyed in the past decade, especially for optical sensors. Such sensors allow the reading of the color of the plant canopy, which is used to infer the amount of input that the plant requires. These sensors typically have a light source that illuminates the canopy through the semiconductor (LED), and the reflected light is captured by optical sensors. Figure 3.6 shows a possible structure for a system of sensing and application of On-The-Go inputs. The data stream captured by one or more sensors is stored (memory or database) and processed (embedded controller), generating a control action for valves of application of inputs (actuators). FIGURE 3.6 – SYSTEM OF SENSING AND APPLICATION ON-THE-GO

Controller and database

sensors

actuators

Source: Sousa, R. et al, 2014.

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Desription

AUTOMATION OF MACHINERY AND AGRICULTURAL IMPLEMENTS: EMBEDDED ELECTRONIC, ROBOTICS AND INFORMATION MANAGEMENT SYSTEM (continuation) At the international level, research in automation and robotics for agriculture has become a prominent issue. In Brazil, it is still very limited, and the potential for innovation is little explored (Sousa, R. et al, 2014). An emerging area of study concerns the autonomous precision agriculture. Today, most automatic farm vehicles used for the detection of weeds, pesticide dispersion, earthworks, irrigation and all other agricultural activities are manned. In the future, autonomous vehicles will be at the heart of all precision agriculture applications (Leite, M. et al, 2014). Research and development of automatic controllers with specific agricultural tasks and their respective implements, in view of the diversity of existing agricultural tracts and implements explored (Sousa, R. et al, 2014).

Challenge/opportunity

Inexistence of departments for the development of embedded electronics in agricultural machinery and equipment factories and mainly domestic manufacturers of implements and the total lack in the market of suppliers of proprietary electronics for agricultural applications. Overcoming these barriers requires from companies the need for significant investments and also for the training of a specialized workforce. Research and development of agricultural methodologies and technologies applied to automatic data acquisition and to the smart control of operations in processes of plant and animal production that have automatic interfaces for integration of information from these systems with management systems. Standardization and reference models, both to enable acquisition, communication, integration and handling of identified or geo-referenced data and to allow the processing of such data into information listing variables and different parameters such as soil data, biological attributes of crops and climatological parameters, to support the decision making process. Training of qualified professionals for research, development and application in automation and mechanization oriented for agriculture and livestock. In the area of autonomous precision agriculture, an open question concerns the interaction of machines with field workers. Machines may not be a threat to workers. They need to be able to recognize them and interact with them during the process of fulfilling their tasks in the field. Another issue relates to the maneuvers of autonomous machines. Navigation, localization, orientation and turning skills require specific strategies which are directly related to the provision of the environment and the vehicle resources. In this sense, a machine-field adaptation is required. Finally, another open point refers to the definition of the sequence of tasks to be performed. The system should be able to manage the available resources in order to optimize the tasks (Aut Cheein; Carelli, 2013, cited in Leite, M. et al, 2014).

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AUTOMATION OF MACHINERY AND AGRICULTURAL IMPLEMENTS: EMBEDDED ELECTRONIC, ROBOTICS AND INFORMATION MANAGEMENT SYSTEM (continuation) Project of the Fund for the Agribusiness Sector CT – Agro: coordinated by Embrapa Instrumentação, the project unites companies, research groups from USP and CTI and aims to encourage, in the domestic market, technologies related to the Isobus standard for tractors and especially for agricultural implements. Project tractors and agricultural and forestry machinery - serial network for data control and communication: one of the goals is to design an Electronic Control Unit with Isobus to enable the construction of a standardized automation system for agricultural machinery and implements, also including application on moving agricultural robots.

Projects, groups and research lines

With regard to autonomous vehicles, company John Deere has developed a prototype of an automatic tractor using satellite signals to follow pre-programmed routes without a human driver. Company Kinze Manufacturing uses a similar approach to their autonomous machine solution (Bauckhage, et al., 2012, cited in Leite, M. et al, 2014). There are scientific researched focused on the roots of plants which, although invisible, they carry great intelligence, gathering information on the physical properties and chemical composition of the soil, using this information to decide in which direction they will continue to grow. Added to this, they can pierce the ground using just a fraction of the energy consumed by artificial power drills, as well as being considered highly efficient systems for underground exploration. There already are researches that developed robotic devices that act like the roots of plants, which purpose is to build robots that can monitor soil pollution, detect minerals and water, allowing better management of underground reservoirs (Robot Plants…, 2013, cited in Leite, M. et al., 2014). The University of Illinois developed a generation of multiple autonomous robots that move following the plantation lines in order to, in the long term, assume some of the functions currently performed by largesized equipment. The idea is to put a few robots out in the field, communicating with each other, to manage and collect data (Peterson, 2014, cited in Leite, M. et al., 2014). A future research line concerns the possibility of exchanging information between robots, that, mirroring the behavior of bees that come out in search of nectar and return to share the information, they may find weeds and report this find to other robots so that they can act together. Robots may be programmed and equipped to undertake functions such as detecting diseases, weeds, insects, soil sampling or even precisely applying pesticides or fertilizers. While these experiments focus on assigning human capabilities to robots, allowing them to be able to perform functions associated with people, whether in the field or in other areas of work, on the other hand, there are studies and experiments that incorporate robotic elements in humans. They are called cyborgs or trans-human. Cybernetic brain implants, along with artificial intelligence and augmented reality, can be integrated into people’s daily lives and change their personal behavior. [...] IA, with overlapping of RA, can radically improve human capacity and skills to carry on their activities (Munkittrick 2011, cited in Leite, M. et al., 2014). Humanization of robots, or human robotization, treats numerous future possibilities aimed at increasing the ability to deal with the world. Ethical issues will surely arise to guide the technological advances and their implications in this area.

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AUTOMATION OF MACHINERY AND AGRICULTURAL IMPLEMENTS: EMBEDDED ELECTRONIC, ROBOTICS AND INFORMATION MANAGEMENT SYSTEM (continuation)

Software available

There are currently available on the market some types of optical sensors with the purpose of reading crop conditions in real time to control the application of nitrogenous fertilizers or to control the localized application of pesticides. Enalta (www.enalta.com) manufactures equipment and develops programs for management and optimization of the entire operational process, with solutions of partial or total automation of activities. The solutions include an electronic controller for spraying, a collector of field activity data, a planting monitor for automatic planters, fertigation controllers, a system of agricultural geographic information and monitoring of the variability in productivity. Falker (www.falker.com.br) has electronic meters of soil compaction, irrigation control and evaluation of plant chlorophyll content, aimed at the nutritional monitoring and correction, if necessary, of fertilization. Their solutions in precision agriculture include collection and analysis of agronomic information and application at variable rates. STA Máquinas (stamaquinas.com.br) sells, in the Brazilian market, Ferrari’s Smart Hoe. The machine recognizes the plant of interest and removes weeds from the ground around it. Its use is oriented to seedlings and vegetables. The company also offers an automatic planter of seedlings.

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Description

Some trends in AgroTIC stand out in the area of knowledge and information management (Pierozzi Jr., I. et al., 2014): • Adoption of IT to obtain, analyze, disseminate and view data, information and knowledge. • Maintenance of interdisciplinary, multi-institutional and transnational teams for the solution of complex problems. • Growth of supply of public data in accordance with official policies of open data. • Adoption of computational intelligence in the development of applications. • Growing use of communication channels and new ways of presenting content, following the evolution in language (Web 2.0 and Web 3.0). • Increased supply of architectures for the development of systems of heterogeneous, distributed data storage, in high volumes and with high generation frequency. In the new context, high-performance computational infrastructure and support will be required. Solutions will need to meet interoperability requirements, with maintenance of virtual environments for collaborative work intermediated by social media and access to data and information through mobile devices, particularly cell phones and tablets.

State-of-the-art

Information management has been predominantly focused on bibliographical and documentary information. However, most recently, initiatives for integration and significance of data and interrelations of information have been carried out in order to enable the construction of a computational infrastructure oriented to the possibility of generating, managing and extracting knowledge (Pierozzi Jr., I. et al., 2014). Information technologies (data banks and databases, information systems) have been developed and implemented based on the assumptions that a particular mastering of knowledge should be decomposed into constituent elements. Once identified, individualized and described (metadata), these constituent elements should be compartmentalized in the tables of the models of entity-relationship of databases or in the navigational menus of the online information systems, for example. This type of approach conditions the following stages of information management (handling, access, retrieval and dissemination of information) to an emphasis on the syntactic aspect at the expense of the semantic understanding, limiting the potential of information transforming into knowledge. The solution lies in the adoption of conceptual models of organization of knowledge that allow cognition to freely transit from the parts to the whole and vice versa. The new approach provides conditions for insertion of informational contents in the context of the Semantic Web (Web 3.0), a trend of technological evolution of the Internet, in which digital information gains significance, computers gain intelligence and content become more pragmatic and effective.

Challenge/ opportunity

Creating knowledge organization systems (KOS) capable of converting data into information, and information into knowledge through the ability of giving meaning to digital information (Semantic Web). These systems may be translated into computational languages (RDF, SKOS, OWL), enabling human knowledge to be read and understood by machines (Pierozzi Jr., I. et al., 2014).

Projects, groups and research lines

KNOWLEDGE AND INFORMATION MANAGEMENT – THE SEARCH FOR SEMANTIC INTEROPERABILITY

Project of management of information and knowledge on agribusiness (Embrapa Informática Agropecuária): the goal is to build a KOS system that has a theoretical and practical framework of Artificial Intelligence and of the Natural Language Processing, a sub-area of artificial intelligence and linguistics which studies the problems of automatic generation and understanding of the natural human languages.

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Description

SIMULATORS IN AGRICULTURE Simuladores são softwares que fazem uso de modelos matemáticos e algoritmos para representar um sistema. Um dos grandes atrativos dos simuladores está em prover um ambiente virtual que permite interagir com representações de sistemas, naturais ou artificiais, sem as limitações do mundo real (Barioni, L. et al., 2014).

State-of-the-art

In agriculture and livestock, there is a growing application of simulators in various areas such as development and production of plants and animals; support to managerial decision-making; dynamics of pests, diseases and contaminants; environmental impact assessments; dynamics of land use; water management; and assessment of technologies in general. With the plethora of initiatives of development of simulators in agriculture and livestock, some of the largest research companies in the area have allocated teams dedicated to their own frameworks and simulation infrastructure (Barioni, L. et al., 2014). Topics such as food safety, mitigation and adaptation to climate change and international trade have been the major claimants of more complex simulators. These topics have required simulations with very wide scope of space and time, generating much more demand for processing than the simulators of productive systems in the beginning of the millennium. Although simulations keep being made based on populations, the use of sensors that collect phenotypic information in real time and the parameterization of models with genomic information have opened the possibility of simulation based on individuals and the use of simulators directly into the genetic improvement and management of systems of production of plants and animals.

Projects, groups and research lines

Challenge/ opportunity

In other industries, important applications of simulation include an optimized control of systems and training of people, areas still incipient in agriculture and livestock. Moreover, in many areas, there is a more consistent integration with other computational techniques, such as those related to computational intelligence, optimization (i.e., simulation-optimization) and robotics. It may be noticed that both precision agriculture and animal husbandry walk towards the other industries more closely related to engineering.

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Use of simulators for training people. Training through games in which realistic simulators are used interactively may promote an important experience to manage problems with productive systems in price and climate risk situations. Moreover, such an approach may end up facilitating teaching due to the possibility of representing concepts related to feedbacks which occur in productive systems. The development of agricultural simulators in Brazil seems to suffer from lack of integration between teams of mathematical modeling, software development and domain experts with networking field research for the development of simulators. This difficulty in integrating teams ends up facing a wall, as always, in the form of lack of education and training of professionals in agricultural sciences and lack of professionals of exact sciences with an interest in agricultural and livestock applications. Embrapa Informática Agropecuária has invested in the generation of tools for use by professionals with training in agricultural sciences. The development of these tools has occurred along with the training of professionals and the formation of research networks. The initiative aims at supporting the new simulation challenges in agriculture, through a holistic and integrated view, which begins in data generation and ends up in the solution of a research problem assisted by the development of a simulator.

SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

3.3 PRODUCTS AND SERVICES FOR AGROTIC OFFERED IN THE BRAZILIAN MARKET This section explores AgroTIC`s products and services available in the Brazilian market. Its characteristics and attributes are discussed. The attempt is to answer the following question: are these products and services aligned with the new trends in AgroTIC?

PRODUCTS AND SERVICES OFFERED BY THE COMPANIES PARTICIPATING IN THE SW AGRO STUDY Below, from visits to the site and searches for online information in June 2016, products and services from 112 companies of the 162 that took part in the Sw Agro-Embrapa research, conducted in 2009, were evaluated. From all those evaluated, seven amongst them had a website whose known address directed the visitor to another company. Thirty-eight companies were not found. At the time of the visits, their website URL was either unreachable or under construction. The attempt to find them through Google searches proved fruitless, an indication that they are no longer available. In the case of 12 other companies, the offers mentioned on their website had no apparent relation to the AgroTIC market, and that is why they were not considered in the analysis (Table 3.1). TABLE 3.1 – CURRENT SITUATION OF COMPANIES PARTICIPATING IN THE SW AGRO-EMBRAPA RESEARCH, CONSIDERING THEIR WEBSITE URL AND ONLINE SEARCHES Total: 162

CURRENT SITUATION

COMPANIES

%

105

64,8%

7

4,3%

112

69,1%

Business do not include AgroTIC products and services

12

7,4%

Website not found or under construction

38

23,5%

Subtotal

50

30,9%

162

100,0%

Company with offer for AgroTIC Address redirected to the website of a different company Subtotal

TOTAL Source: Softex Monitoring Centre, based on data from Sw. Agro/Embrapa research.

The 112 companies found and with products for AgroTIC currently offer 308 products, 2.8 products on average per company. Sixty-one companies, i.e., a little over half (54.5%) offer 95 products for administration/ management of farms, including, in some cases, business modules (accounting, finance, human resources, etc.) and, in others, only solutions for management of agricultural and livestock activities (crop planning, control of production or flock, information systems, use of pharmaceuticals, etc.). Most of the 95 products available for administration/management of farms (41.1%) may be used indifferently for the management of establishments oriented to animal management or to plant management. A lower amount is specialized in animal management (34.7%) or vegetable management (24.2%) (Table 3.2).

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TABLE 3.2 – DISTRIBUTION OF SOFTWARE FOR ADMINISTRATION/MANAGEMENT OF THE FARM, ACCORDING TO THE CATEGORY CATEGORY

PRODUCTS

%

Only animal management

33

34,7%

Only vegetable management

23

24,2%

Animal + vegetable management

39

41,1%

Total

95

100,0%

Source: Softex Monitoring Centre, based on visits to the website of the companies and Google search.

Within the set of products oriented for administration/management of farms geared for animal management, specialized solutions are predominant in a given type of flock. In this case, products for (beef and/or dairy) cattle and poultry stand out (Table 3.3). TABLE 3.3 – DISTRIBUTION OF SOFTWARE FOR ADMINISTRATION/MANAGEMENT OF THE FARM, ORIENTED FOR ANIMAL MANAGEMENT, ACCORDING TO THE SUB-CATEGORY SUB-CATEGORY

PRODUCTS

%

12

36,4%

Only poultry (and eggs)

9

27,3%

Only swine

3

9,1%

Only fish and/or seafood

3

9,1%

Only sheep/goats

2

6,1%

Herd in general

4

12,1%

33

100,0%

Only (beef and/or dairy) cattle

Total

Source: Softex Monitoring Centre, based on visits to the website of the companies and Google search.

Within the set of products for administration/management of farms geared for vegetable management, the majority (39.1%) is indicated for any type of crop. Products with greater specificity are geared for sugar- alcohol producing establishments (26.1%) and forestry systems (21.7%) (Table 3.4). TABLE 3.4 – DISTRIBUTION OF SOFTWARE FOR ADMINISTRATION/MANAGEMENT OF THE FARM, ORIENTED FOR VEGETABLE MANAGEMENT, ACCORDING TO THE SUB-CATEGORY SUBCATEGORY

PRODUCTS

%

Only sugarcane

6

26,1%

Only forestry systems

5

21,7%

Only grain

1

4,3%

Only cotton

1

4,3%

Only fruit

1

4,3%

Two or more of the options mentioned

9

39,1%

23

100,0%

Total

Source: Softex Monitoring Centre, based on visits to the website of the companies and Google search.

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Eighty-two companies (73.2% of the total), some of them also with supply for software for administration/ management of farms, have products that control specific agribusiness processes. These companies offer, in all, 213 products (69.2% of the total) (Table 3.5). There are several types of processes and the products under examination are distributed among them. Most are for post-harvest processes or animal processing (22.5%). In second, are the offerings of products and services involving the geo-technologies. This category consists mainly of companies which are vendors of third-party solutions, in general, of foreign brands and/or service providers on demand. Inventory/forest management is also a process in which companies work primarily in a model of provision of services. In the set of products for fertilization/liming and soil in general, most offers are still of software packages, which help the process of decision on fertilizers, from the results obtained with the soil tests performed in the laboratory. However, there also are companies that offer equipment for monitoring and continuous analysis of the soil through remote sensing and recommendation of use of fertilizers on a case-by-case basis. Products for traceability, with a proposal within the food safety perspective (i.e, from the field to the consumer’s table), account for 7.5% of the total. Overall, company suppliers of solutions have internet portals or portals which are suitable for mobile devices in order to facilitate consumer access. Almost 5% of the products offered are for phytosanitary purposes and management purposes to prevent pests. Most of the offer is also based on a software package. However, there already are smart proposals that, with support from equipment with computer vision and aerial imaging, manage to identify plant diseases, to suggest specific locations, and certain doses of pesticide use. The same occurs for irrigation (3.8% of products): software packages are predominant, but, in the set of companies, there already are some that offer products for monitoring and constant analysis of presence of water in the soil, with recommendations for irrigation in the required amount, considering specific locations. Many products for administration/management include management of a fleet of vehicles and machines in their ERPs. Some (4.2% of the total), however, are dedicated solely to management of fleets and machinery. TABLE 3.5 – DISTRIBUTION OF SOFTWARE FOR PROCESSES, ACCORDING TO THE TYPE OF PROCESS TYPE OF PROCESS

COMPANIES

%

Post-harvest/animal processing

48

22,5%

Geo-technologies

23

10,8%

Forest inventory/forest management

18

8,5%

Fertilizer, lime/soil

16

7,5%

Traceability

16

7,5%

Phytosanitary/management prevention pests/prescriptions

10

4,7%

Fleet management

9

4,2%

Portal/database

9

4,2%

Irrigation

8

3,8%

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TYPE OF PROCESS

COMPANIES

%

Animal feed/nutrition

8

3,8%

Indicators/BI

7

3,3%

Seeds/inputs

7

3,3%

Planning

7

3,3%

Agro-metereology/environmental management

2

0,9%

Bioinformatics/genetics/artificial insemination

4

1,9%

Quality control

3

1,4%

Records of field activities

3

1,4%

Contract/business/credit

2

0,9%

Laboratory

2

0,9%

11

5,2%

213

100,0%

Other processes (more than one group of processes) Total

Source: Softex Monitoring Centre, based on visits to the website of the companies and Google search.

ANALYSIS OF RESULTS The supply of products is diversified, covering the different cultures and various processes. Companies are spread out throughout the country, serving, in general, a particular region of the territory, where they maintain physical presence. The product portfolio is still quite based on desktop solutions. But there are also products to be used online and, to a lesser extent, solutions for mobile devices. The business model adopted by the companies is the traditional use license. Supply of software packages share the space with supply of solutions that require customization services, technical support and training. This, in fact, is the predominant type of business. There are few companies working in the SaaS model or using proprietary software to offer high-value services (ITES model). In general, this type of business tends to be adopted in some specific segments of operation. For example, among companies dedicated to conducting forest inventory or between offerors of spraying services using their own aircrafts. In the list of companies assessed, the number of those that offer solutions involving hardware is small. Sensors for precision agriculture and embedded software in agricultural machinery and implements account for a very small part of the supply. The presence of large players, usually foreign capital companies, focus on providing communication infrastructure through telecommunications operators; infrastructure software, through the platform leaders of the software sector; and hardware, including in the category, agricultural machinery and implements with embedded software/sensors. Several among the brands are represented by national retailers who provide support and consulting services.

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The market for applications and IT services is still very much dominated by companies funded with domestic capital. The presence of large ERP players in the agribusiness market, offering specific software modules for farm management, is not common. Solutions of small- and medium-sized companies that operate generally in specific productive chains and in parts of the national territory are predominant. Probably one of the reasons that has led the major players to avoid the agribusiness and, more specifically, the agriculture and livestock segments, has to do with the huge variability of the rural environment when compared, for example, with the industrial environment. The variation prevents the adoption of generic solutions, elevating development costs. In general, products and services offered by companies under analysis are limited to the borders of the farm, dealing specifically with events and processes that take place from the gate in. Analytical reports obtained from this cutout also tend to consider exclusively the data collected at the farm. The supply of products for AgroTIC of the companies surveyed still requires a relevant involvement of people for appointment tasks, in processes also characterized by semi-automation. Although the pretension of companies tends to be to cover a wide range of productive agribusiness chains, their customers, in general, still seem very much focused on a particular type of crop or animal species. Thus, for example, it is common to find software that, according to the company, covers all the animal husbandry needs, but, however, it has been used very especially by pig farmers. The operation of the companies is also very much located in the national territory and, within it, customers are concentrated in some regions more or less comprehensive. Few mention marketing abroad. Comparing the results of products and services offered by the companies in 2009 and the current portfolio, it is possible to notice changes in order to update and expand the products and migrate to more modern platforms, especially involving web solutions. The supply of products is consistent with the existing demand. The rural environment is still little computerized, except for some productive chains and large farms. Thus, existing solutions tend to be directed to the agribusiness chains with greater potential for generation of revenue. For many farms, professional management, supported by IT, is still the first step to be taken towards computerization. The communication infrastructure, a key piece to the processes of computerization, since they support data transfer and communication between the field and the farm, is feeble and expensive.

NEW OFFERS FOR AGROTIC The new technologies tend to influence IT suppliers and accelerate the computerization processes in agribusiness, even though this is, in general, a sector that is more averse to new things. Thus, in a world level and also in the domestic market, in addition to the perceived changes in companies that have been active in the industry for decades, suppliers of AgroTIC which are already born geared to new technologies and business models begin to emerge (Box 3.2).

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The analysis of products offered by startups operating in the agribusiness sector highlights the differences with the supply of the generation of companies that have emerged in past decades. It is possible to notice significant changes in the specification of products, in the focus, in the form of delivery, in the business model, etc. In general, products are much more specific. Consider, for example, the product offered by startup Pomodore, mentioned in Chapter 2, that will help tomato growers to monitor and analyze key data (temperature and humidity of air and soil) from sensors in real time. In the same line, it is worth mentioning the product of startup Smarthoney, which aims to monitor the situation in and out of the hive with the support of sensors. The results are available online and can be seen in smartphones. They do not require special training or support for immediate use by the interested user and do without the work of humans to collect data. For recommendations and issuances of warnings, they can be based, for example, in pictures, sounds, sensors and data received from various sources. It is also possible to notice a greater interest of the new entrants for segments still not very much covered by the supply of traditional companies. This is the case, for example, of the supply for bioinformatics, predictions for harvesting and warnings for decision making. Concerning the decision processes, the trend goes in line with combining different sources of data, extrapolating the limits of information that may be obtained from the gate in. It is also possible to notice a greater trend for the use of hardware in proposals include the provision of services (ITES). In this category, are the drones and remote sensors. BOX 3.2 - COMPARISON BETWEEN TRADITIONAL BUSINESSES AND NEW BUSINESSES IN AGROTIC ITEMS

TRADITIONAL BUSINESSES

NEW BUSINESSES

Business type

Product – package or customizable and software development services

Micro-services; ITES; software embedded in agricultural machinery and implements

Specialization degree

Low/moderate

Moderate/high

Interaction with hardware

Weak

Strong

Type of license

Traditional license

SaaS or ASP

Devices

Desktop, web

Web, apps

Focus

Every plant, every animal

A plant; an animal

Who does it?

The farmer

Automatic system

Data collection

The farm

Combination of various sources of data

Source: Softex Monitoring Centre.

3.4 CHANGES TO THE STRUCTURE AND DYNAMICS OF THE AGRIBUSINESS SECTOR An example of sustainable model of the future: the smart farm. Figure 3.7 shows a schematic model of the smart farm of the future designed in a project of the European Union. This is an open network example that is mentioned in Section 3.1, i.e., a situation in which the use

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

of platforms that encourage interoperability and the open format for data exchange and specialization of companies in links of the value chain is predominant, a stimulus to the emergence of an ecosystem composed of many players. In the case of agribusiness, specialization means the presence of companies dedicated to specific topics in each of the various segments of the management process of the smart farm, heavily based on data handling: collection and local processing; transmission; and storage, processing and analysis. Precision Agriculture masters the initial process of collection and local processing of data. The collection is made by sensors distributed around the farm, embedded in equipment or carried by drones, aircrafts, etc. The collectors will be able to capture/save energy and will be identified by RFID technology. Along with RFID, the encryption applied to light devices; nanotechnology; and the new substrates for development of integrated circuits and antennas are among the technologies enabling of the new reality at the farm. Near field wireless communication networks and machine-machine communication will be used for the exchange of data and information between the various devices and between them and the local management system of the farm. The data collected at the farm will be processed by a local management system, providing relevant information to farmers. Through fifth-generation networks, with security, performance, scalability and quality of adequate services, this data will be stored in the cloud, where it can be combined with data and information from various sources (weather, price of commodities in the various markets, exchange rates, availability of warehouses and cargo transport, etc.), including those from other farms. Processing and analysis of this large volume of data can generate a multitude of micro-services to be provided by different providers to the participants of the network. The cloud environment works as a virtual market, a large store in which different players get together virtually to do business. The open format architecture enables data sharing and exchange of information between the different players and the easy replacement of a supplier for another. In the virtual market, players have access to a number of reliable, sophisticated and intelligent services. Using appropriate generic facilitators, the composition and mixture of data and information will provide several new services. For example: •

Automated consulting, including analysis of collected data and recommendation of appropriate actions for spraying the plantation and other tasks.



Scheduling of field activities, organizing tractors from spraying companies to take on contracts entered into in a given area.



Information to the farmer, such as geospatial and meteorological data and dynamic update of firmware of rural machinery.



Information to the end user on production of food, including the use of chemicals and pesticides and methods of cultivation used, etc.



Information to the distributor on, for example, the availability of production: when, where, how good, how many.



Marketing for different players, allowing the upload of information on services provided, pictures and videos, online shopping, etc.

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FIGURE 3.7 – SCHEMATIC MODEL OF THE SMART FARM OF THE FUTURE ■■ Remote sensing (drones; aerial pictures for land coverage); GIS (image processing) ■■ Capture/conservation of power

FARM

■■ Encryption in light devices

Sensors

■■ RFID – identification/ sensing ■■ Nanotechnology

Local management system



INTERNET OF THE FUTURE Cloud management system

Smart machines

■■ Integrated circuits – antennas on new substrates ■■ Smart processing

■■ SERVICES at a large store/market: meeting point of various players: farmers, technology suppliers, comparators, consultants, sponsors, etc.

■■ NFC; Bluetooth, WPAN, WLAN, cell phone networks, wireless sensor network, M2M systems

COLLECTION/PROCESSING

■■ IPv6 ■■ 5G Networks ■■ Safety, performance, integration of services, scalability, QoS

TRANSMISSION

■■ Big data; laas, PaaS, SaaS; GIS; data mining; image/ pattern recognition; tracking

STORAGE, PROCESSING & ANALYSIS

Specific applications Generic domain

Source: Kaloxylos, A. et al. (2012).

Use of open data. One of the premises for the smart farm to become a reality is the availability for use (and reuse and crossing) of the data collected in the various farms and in other sources. It is undeniable that the exchange of data and information is capable of generating high-value knowledge. However, in order for the model to work, farmers need to realize the benefits resulting from their participation in the initiative. The interest in contributing will come from individual perception of the gains that an architecture of this nature is able to provide. This means that, in order for it to work, the system will have to find mechanisms to conveniently monetize each player for the relevant data and information they provide. Precisely because the farmer owns important assets for the construction of the smart farms of the future, as a reward for their contribution, they can ultimately enjoy free or low cost services. The possibility of existence of a common market, where data and information flow freely, will require a significant change to the habits and relationships of the players participating in the agribusiness value chain. In various productive chains, information is a valuable asset that needs to be kept under lock and key, for the secret is essential to obtaining the best prices in the market. Thus, for example, nowadays, the farmer has no interest in revealing the amount of cattle he has on pasture in conditions of being slaughtered, because the price for their meat fluctuates due to the estimate of the available feedstock.

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Sustainability could be even bigger. In the new context in which farms need to increase their productivity gains, the sustainable use of production factors, including data, information and knowledge, becomes increasingly critical. Apart from the economics side, the conscious and efficient use of production factors is also an environmental requirement. The sustainability which may be obtained by the reuse of data and the financial savings that may end up benefitting every participant or the network as a whole could be even greater in the case, for example, of the smart farm (or group of farms) generating their own energy that they consume or recycling waste and water they use. The use of photovoltaic cells to capture solar energy or biomass, for example, would make the farm less dependent on the energy from traditional generation systems. The energy surplus could be sold to service providers, generating more resources for the farming business. The open network model may help leapfrogging in AgroTIC, taking technology more easily for small- and medium-sized businesses and building a strong and vibrant digital ecosystem. In Brazilian agribusiness, with small- and medium-sized farming businesses and various productive chains still in an incipient process of adoption of ICTs, the open network model may be a great opportunity for leapfrogging, accelerating the computerization process in the field, making it ubiquitous and pervasive, through the supply of low-cost, high-value services. the open network model is also frankly favorable for the small- and medium-sized Brazilian companies of software and IT services, as it allows them to benefit from a large volume of existing data and information to create their supply of services. Models of oligopoly and conglomerate are also a possibility In precision agriculture, a whole cycle of agribusiness knowledge and management processes may end up closing in upon itself, keeping the logic from the gate in. The embedded electronics enables machines to collect data or make decisions for action right after. The data collected may be used only by the farmer or be taken advantage of by the companies responsible for the collecting machines, which also start specializing in the handling and processing of data and information. Embedded electronic systems that handle the end-to-end value chain would greatly limit the room for the actuation of small- and medium-sized companies of the industry. In principle, the existence of an ecosystem composed of a large number of companies is desirable because the competition tends to make the prices go down, providing other options for consumers. In addition, the renewal of companies encourages innovation and the entry of new products and services to the market. Even in a model of oligopoly or conglomerate, an issue that becomes essential, therefore, concerns the possibility of use (and reuse) of the collected data.

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FINAL THOUGTS New technologies (mobility, social media, cloud computing, big data/analytics, Internet of Things, physical and digital convergence, smart machines, etc.), more and more present in our daily lives are changing people’s lives and how they are organized and interact, affecting the economy as a whole. The various economic sectors are affected by them, including, among them, the agribusiness and ICT sectors. In the agribusiness sector, the new technological trends share the space with climate change, the heavy pressure exerted by population growth over the use of the land already tired and worn, the growing concern over food safety and the health of the planet. Added to this are the great recent achievements related to the findings in Biology and Nanotechnology and the convergence of these new sciences with Computerization and Robotics. In the ICT sector, transformations are perceived in the structure and dynamics of the industry. The relations so far established between players are affected, opportunities emerge for new entrants, in a context still spearheaded by major uncertainties. The focus that used to be provided for management and processes, with emphasis on making them more efficient and fast, is now transferred to business models. Attention is now being focused on the search for sustainable models, human, invisible, pervasive and based on the context of usage. Several models of organization of the ICT companies focused on the agribusiness sector (AgroTIC) emerge in the new context. Two aspects are relevant in the differentiation of these alternative models. One of them concerns the interoperability and use of universal standards for the exchange and sharing of data, i.e., if the platforms will be open or closed. The other is related to the positioning of the companies in the AgroTIC value chain, i.e., if the vertical concentration will or will not prevail over specialization. In the model of oligopoly, the platforms are proprietary and there is a high vertical concentration. In the conglomerate, the open platforms are predominant; vertical concentration is high. In the Star Network model, somewhat similar to the structure and dynamics of the IT industry in existence until then, the platforms are proprietary, but there is room, in specific niches, for the permanence of specialized companies. In the open network model, open standards and specialization are predominant. The open network model may help leapfrogging in AgroTIC, accelerating the process of adoption of technology in small- and medium-sized farms. It also offers great opportunities for startups, small- and medium-sized ICT companies, as it stimulates the creation and strengthening of a strong and vibrant digital ecosystem, fueled by the sustainable and collaborative use of data and information, key elements, actually essential for the sustainable economy of knowledge.

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3.A1 – LIST OF SOFTWARE SUPPLIERS FOR AGRIBUSINESS

The following list complements the list of companies with AgroTIC products and services presented in Appendix 2.A2, List of software suppliers for agribusiness, of Chapter 2 that participated in the Sw-Agro survey conducted by Embrapa Information Technology in 2009. The list below provides companies not located at the time and also new entrants in the sector, including active startups in the country. #

COMPANY

1

AGPR 5

SC

http://www.agpr5.com

2

AGR Agricultura de Precisão

MT

www.agronline.net

3

AgriForte – Agricultura de Precisão

GO

www.agriforte.com

4

AgriGeo Tecnologias e Serviços

SP

www.agrigeo.agr.br

5

Agrival

RS

6

Agro 1 Tecnologia da Informação

RS

www.agro1.inf.br

7

Agro GPS Agricultura de Precisão

GO

http://agrogps.webnode.com/

8

Agrologia

RS

www.pluviometro.com.br/

9

Agrologic

RS

www.agrologic.com.br

10 Agropixel

PR

agropixel.com.br

11 AgroPlan Serviços Agrícolas

RS

www.agroplan-rs.com.br

12 Agroprecision Serviços Agrícolas

RS

www.agroprecision.com.br

http://facebook.com/ agriculturainteligente

13 Agrosmart

SP

www.agrosmart.com.br

http://facebook.com/ agrosmart1

14 Agrosolos em Tecnologia de Precisão

MT

15 Agrosystem

SP

agrosystem.com.br

16 Agrotecnologia Produtos e Serviços

PR

agrotecnologia.agr.br

http://facebook.com/ agrotecnologia

17 Airjob Auditores e Consultores

PE

www.airjob.com.br

http://facebook.com/airjob. auditores

18 Algrano

ES

www.algrano.com

http://facebook.com/ algrano.coffee

SC

www.amplaagricultura.com.br

http://facebook.com/AmplaAgricultura-de-Precisao

20 Analissolo

PR

www.analissolo.com.br

21 APAgri Consultoria Agronômica

SP

www.apagri.com.br

22 AP Geotec - Agricultura de Precisão

SP

23 AP Soluções Tecnológicas

MS

www.apst.com.br

24 APx Agricultura de Precisão

PR

www.apx.agr.br

MT

http://atuallab.blogspot.com.br

19

25

Ampla Tecnologia de Precisão e Consultoria Ambiental

Atual Laboratório de Análises Agronômicas

STATE WEBSITE

FACEBOOK http://facebook.com/agpr5.

http://facebook.com/Agrival http://facebook.com/agro1ti

http://facebook.com/ agropixel.agr.br

http://facebook.com/ AgroSolos-Tecnologias-emAgricultura-de-Precisao

http://facebook.com/ APGeotec

http://facebook.com/APxAgriculturadePrecisao

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CHAPTER 3 - PROSPECTS AND PREDICTIONS FOR ICT IN BRAZILIAN AGRIBUSINESS

#

COMPANY

STATE WEBSITE

FACEBOOK

26 Auteq Telemática1

SP

www.auteq.com.br

27 Base Assessoria Agronômica

RS

www.base.agr.br

http://facebook.com/BasePrecisao-na-Agricultura

28 Biossolo Consutoria e Projetos

SP

www.biossolo.com.br

http://facebook.com/ Biossolo

29 Boreste Sistemas Embarcados

SC

www.boreste.com

http://facebook.com/boreste

30 Bov Control

SP

www.bovcontrol.com

MS

www.bwt-brasil.com.br

32 CellSeq

MG

www.cellseqsolutions.com.br

33 Ceres Agrotecnologia

SC

34 Cooperativa Farol

RS

www.cooperativafarol.com.br

35 Drakkar Solos

RS

www.drakkar.com.br

http://facebook.com/ DrakkarSolos

36 DronEng

SP

www.droneng.com.br

http://facebook.com/ Droneng.br/

37 E-Aware

RS

www.eaware.com.br

38 Fibra Agrotecnologia

MT

39 Futura Agriculture

MT

http://futuraag.com.br

40 Geo Agri Tecnologia Agrícola Ltda

SP

www.geoagri.com.br

41 Geosafra Agricultura de Precisão

MS

geosafra.net.br

42 Geoterra Agricultura de Precisão

MS

http://www.geoterra.com.py/

43 Grupo Analys

RS

www.grupoanalys.com.br

http://www.facebook.com/ Analys-Agricultura-dePrecisão

44 Herbicat Ltda.

SP

www.herbicat.com.br

http://www.facebook.com/ herbicat

45 Horizonte Agricultura de Precisão

MT

46 Ibra Agrisciences

SP

www.ibra.com.br

47 Idealsis – Sistemas Corporativos

SP

www.idealsis.com.br

48 Impar Agricultura de Precisão

BA

http://www.imparag.com.br/

SP

inceres.com.br

http://www.facebook.com/ inceres

MG

irriger.com.br

http://www.facebook.com/ Irriger

31

49

BWT-Consultoria e Sensoriamento Remoto

Inceres Desenvolvimento de Software e Processamento de Dados

50 Irriger Connect

http://facebook.com/CeresAgrotecnologia

http://facebook.com/fibra. agro

http://www.facebook.com/horizonteagriculturadeprecisao

1

1 The Auteq Telematics, a Brazilian company of embedded software and computing, was acquired in 2014 by Deere & Company. Previously, in 2009, John Deere had created a joint venture with Auteq to provide technologies and integrated and innovative solutions for sugarcane production. The acquisition gives the John Deere additional expertise in the sugarcane market and greater ability to develop products and services to help customers use the data produced by computers shipped in equipment used for planting, crop management and harvest in the sugarcane context. In addition to software, Auteq was also specializes in hardware.

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SPECIAL ISSUES OF THE MONITORING CENTRE: ICT IN AGRIBUSINESS

#

COMPANY

STATE WEBSITE

FACEBOOK

51 Jacto Máquinas Agrícolas

SP

www.jacto.com.br

52 John Deere Brasil Ltda.

SP

www.deere.com.br/pt_BR

53 Kajoo

SP

www.kajoo.com.br

54 Khor TI

SC

http://www.khor.com.br

55 Latitude 23

SP

http://latitude23.com.br

56 Lenke Automação

SC

http://www.lenkeautomacao. http://www.youtube.com/ com.br user/lenkeautomacao1

57 LL Cultivar

SP

llcultivar.com.br

58 Meta Agrícola

RS

www.metaagricola.com.br

59 Nutriexacta

SP

www.nutriexacta.com.br

60 Olearys Agropecuária

SP

olearys.com.br

61 Pastar Serviços Agropecuários

www.pastar.com.br

62 Preciza Agricultura de Precisão

PR

www.preciza.com.br

63 Rogue Rovers

EUA

www.roguerovers.com

64 Saci Soluções

SP

www.sacisolucoes.com

65 Scylla Bioinformática

SP

www.scylla.com.br

66 Smart Agriculture

SP

www.smartaganalytics.com

67 Sólida Agroconsultoria e Assessoria

PR

http://www.solidaag.com.br

68 Soyus Agricultura de Precisão

GO

www.soyus.com.br

69 SST Software Brasil

SP

www.sstsoftware.com

70 STA Soluções em Tecnologia

SP

jltecnologias.com.br

71 Stara

RS

www.stara.com.br

RS

www.tecagri.com.br

73 Treevia Forest Technologies

SP

www.treevia.com.br

74 VittaCura do Brasil

PR

http://www.vittacura.com.br/

75 WPS

SP

www.wps.eu

RS

http://www.yarabrasil.com.br

72

Tec Agri Tecnologia em Agricultura de Precisão Ltda.

76 Yara

2

http://facebook.com/JohnDeere

http://facebook.com/ MetaAgricola

http://facebook.com/ pastarSA http://www.facebook.com/ killawatt1000

http://facebook.com/ treeviaforest

1

2 Dutch company represented in Brazil by Flórida Estufas (www.floridaestufas.com.br), located in State of São Paulo.

121

GLOSSARY

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GLOSSARY 3D – Three-Dimensional ABES – Brazilian Association of Software Companies ABINEE – Brazilian Association of Electrical and Electronics Industry AFITA - Asian Federation for Information Technology in Agriculture AgroTIC – ICT applied to agribusiness AI – Artificial Intelligence ANATEL – Brazilian Telecommunications Agency API – Application Programming Interfaces API-Agro - National Program of Agricultural and Rural Development APP – Application for mobile devices AR – Augmented Reality Arpanet - Advanced Research Projects Agency Network ASP – Application Service Provider Assespro – Brazilian Association of IT Companies AZRC – Agricultural Zone of Climate Risk B2B – Business to Business B2B2T – Business to Business to Thing B2C – Business to Consumer B2C2T – Business to Consumer to Thing BBS – Bulletin Board System Brasscom – Brazilian Association of ICT Companies C2C – Consumer to Consumer C2C2T – Consumer to Consumer to Thing CBO – Brazilian Classification of Occupations

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GLOSSARY

CEO - Chief Executive Officer CIGR – International Commission of Agricultural and Biosystems Engineering CNA – Brazilian Confederation of Agriculture and Livestock CNPq – National [Brazilian] Council for Scientific and Technological Development Concar – Planning Committee for a National Infrastructure of Spacial Data CTC – Sugarcane Technology Center CTI – Computer Technology Center Renato Archer DNA – Deoxyribonucleic Acid (Ácido desoxirribonucleico) EFITA - European Federation for Information Technology in Agriculture Emater - Technical Assistence and Rural Extension Company Embrapa – Brazilian Agricultural Research Corporation ERP - Enterprise Resource Planning EU – European Union EUNITA - European Network for Information Technology in Agriculture FAO - Food and Agriculture Organization Fapemig – Foundation for Research Support of Minas Gerais Fenainfo – National Federation of IT Companies EEZ – Ecological-Economic Zoning FMIS - Farm Management Information Systems GDP – Gross Domestic Product GIS - Geographic Information System GNSS – Global Navigation Satellite System GPS - Global Positioning System IAC – Agronomic Institute of Campinas IBGE – Brazilian Institute of Geography and Statistics ICT - Information and communications technology ICT-Agri - ICT and Robotics for Sustainable Agriculture INDE – National Spacial Data Infrastructure INFITA - International Network for Information Technology in Agriculture IoT - Internet of Things IPv6 – Internet Protocol version 6 ISO - International Organization for Standardization IT– Information Technology ITES - Information Technology Enabled Services KOS - Knowledge Organization System LaCTAD - Unicamp Bioinformatics Laboratory

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LED - Light Emitting Diode LMB – Bioinformatics Multi-user Laboratory M2M – Machine-to-Machine MAPA – Ministry of Agriculture, Livestock and Supply MCTI – Ministry of Science, Technology and Innovation MCTIC – Ministry of Science, Technology, Innovations and Communications MTE – Ministry of Labour NCEA – National [Brazilian] Classification of Economic Activities NFC – Near Field Communication OECD – Organisation for Economic Co-operation and Development OSI-model – Open System Interconnection Model OWL – Web Ontology Language PA – Precision Agriculture PaaS – Platform as a Service PROFTIC – professionals formally employed in occupations dealing with ICT QoS – Quality of Service RAIS – Annual List of Social Information RDF – Resource Description Framework R&D – Research and Development RFID - Radio-Frequency Identification SaaS – Software as a Service SBIAgro - The Brazilian Association of Information Technology in Agriculture SKOS – Simple knowledge organization systems SSD – Specific System of Decision SWOT – Strengths, Weaknesses, Opportunities and Threats TARE – Technical Assistance and Rural Extension TFP – Total Factor Productivity UAVs – Unmanned Aerial Vehicles UFJF - Federal University of Juiz de Fora UFLA - Federal University of Lavras UFRPE - Federal Rural University of Pernambuco UFRRJ - Federal Rural University of Rio de Janeiro UFV - Federal University of Viçosa UnB – University of Brasília Unicamp – State University of Campinas US – United States

125

GLOSSARY

USP – State University of São Paulo VRT – Tecnology of Application at Variable Costs WCCA: World Congress on Computers in Agriculture WLAN – Wireless Local Area Network WPAN – Wireless Personal Area Network XML – Extensible Markup Language Brazilian States AC – Acre AL – Alagoas AM – Amazonas AP – Amapá BA – Bahia CE – Ceará DF – Distrito Federal ES – Espírito Santo GO – Goiás MA – Maranhão MG – Minas Gerais MS – Mato Grosso do Sul MT – Mato Grosso PA – Pará PB – Paraíba PE – Pernambuco PI – Piauí PR – Paraná SC – Santa Catarina SE – Sergipe SP – São Paulo RJ – Rio de Janeiro RN – Rio Grande do Norte RO – Rondônia RR – Roraima RS – Rio Grande do Sul TO – Tocantins

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BIBLIOGRAPHIC REFERENCES ALENCAR, J. et al., 2015. “A representatividade das principais empresas agrícolas do Estado de São Paulono agronegócio brasileiro”. Infos Econômicas, SP. V. 45, n. 1, jan./fev., pp. 5 – 19. ALMEIDA, V. F., 1999. In: Revista Agrosoft - 5 Ano. p. 2. Juiz de Fora. BAMBINI, M.D., Mendes, C.I.C, Moura, M.F., Oliveira, R.S.M., 2013. “Software para agropecuária: panorama do mercado brasileiro”. Parc. Estrat. Ed. Esp. v. 18, n.36, p. 175-198. BARBEDO, J. et al, 2014. “TIC na segurança fitossanitária das cadeias produtivas”. ”.In: Masshruhá et al (Ed. Técnicos). Tecnologias da Informação e Comunicação e as suas relações com a agricultura. Embrapa. BARIONI, L. et al, 2014. “Desenvolvimento de simuladores na agropecuária”. ”. In: Masshruhá et al (Ed. Técnicos). Tecnologias da Informação e Comunicação e as suas relações com a agricultura. Embrapa. BUGHIN, J. et al, 2013. “Ten IT-enabled business trends for the decade ahead. As technological change accelerates and adoption rates soar, ten pivotal trends loom large on the top-management agenda”. McKinsey, May. CINTRA, L. et al, 2014. “Métodos, conceitos e técnicas utilizadas na construção de AgroTIC”. In: Masshruhá et al (Ed. Técnicos). Tecnologias da Informação e Comunicação e as suas relações com a agricultura. Embrapa. CNA – Confederação Nacional da Agricultura e da Pecuária do Brasil, 2016. Boletim Agronegócio Internacional, Edição 20, janeiro. DAVIS, J. H.; GOLDBERG, R. A., 1957. “A concept of agribusiness”. Journal of Farm Economics, Ithaca, Vol. 39, Issue 4, pp. 1042-1045, November. DIEGUES, A. C., 2010. “Atividades de Software no Brasil: dinâmica concorrencial, política industrial e desenvolvimento”. Tese de doutoramento, IE-UNICAMP, Campinas.

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MASSRUHÁ, S. et al, 2014. “Os novos desafios e oportunidades das tecnologias da informação e da comunicação na agricultura (AgroTIC)”. In: Masshruhá et al (Ed. Técnicos). Tecnologias da Informação e Comunicação e as suas relações com a agricultura. Embrapa. MENDES, C. et al, 2014. “Uso de computador e Internet nos estabelecimentos agropecuários brasileiros”. In: Masshruhá et al (Ed. Técnicos). Tecnologias da Informação e Comunicação e as suas relações com a agricul- tura. Embrapa. ______ C.; Oliveira, D.R.M.S.; Santos, A.R. (editores técnicos), 2011. “SW AGRO: Estudo do Mercado Brasileiro de Software para o Agronegócio”. Embrapa Informática Agropecuária, Campinas (SP). MONTEIRO, J. et al, 2014. “TIC em agrometeorologia e mudanças climáticas”. In: Masshruhá et al (Ed. Técnicos). Tecnologias da Informação e Comunicação e as suas relações com a agricultura. Embrapa. MORAIS, A. C. P; ALMEIDA, A. N; SPOLADOR, H. F. S; BARROS, C. S. C. B, 2015 “Análise do mercado de trabalho no agronegócio do Brasil a partir dos microdados das PNADs entre 2002 a 2013”, Informações Econômicas, SP, v. 45, n. 4, Jul./Ago. MUNIS, G. (editor), 1997. “Guia Agrosoft de Software Agropecuário 97”. In: Revista Agrosoft, Nº 1, Juiz de Fora. NATIONAL RESEARCH COUNCIL, 1997. Precision agriculture in the 21st century: Geospatial and information technologies in crop management. National Academies Press. OBSERVATÓRIO SOFTEX, 2015. “Software livre: tendências, oportunidades e desafios. Cadernos Temáticos do Observatório, número 4. ______, 2014. “Pesquisa, Desenvolvimento e Inovação em software e serviços de TI”. Cadernos Temáticos do Observatório, número 3. ______, 2009. “Estimativa do valor referente às atividades de software e serviços de TI realizadas na IBSS”. Software e serviços de TI: a indústria brasileira em perspectiva, número 1, volume 1, capítulo 7, págs. 114 - 123. ______, 2009. “A força de trabalho em atividades de software e serviços de TI na NIBSS”. Software e serviços de TI: a indústria brasileira em perspectiva, número 1, volume 1, capítulo 8, págs. 124 - 155. PIEROZZI JR., I. et al, 2014. “Gestão da informação e do conhecimento.”. In: Masshruhá et al (Ed. Técnicos). Tecnologias da Informação e Comunicação e as suas relações com a agricultura. Embrapa. ROSELINO, J. E., 2006. “A Indústria de Software: o modelo brasileiro em perspectiva comparada”. Tese de doutoramento, IE-UNICAMP, Campinas.

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ICT IN AGRIBUSINESS